Interview guides · CARTO

CARTO Data Scientist Interview Questions & Guide 2026

SQL practice for CARTO data scientist candidates: window functions, funnel and retention queries, joins that quietly drop rows.

The 60-second brief

As a Data Scientist at CARTO, you play a crucial role in transforming complex data into actionable insights that empower businesses to make data-driven decisions. This position is pivotal in enhancing CARTO's product offerings, which focus on location intelligence and spatial data analysis. By leveraging advanced analytics and machine learning techniques, you will contribute to developing innovative solutions that cater to a diverse range of industries, from urban planning to logistics.

Allocate prep to your weakest link rather than your favourite topic. Of the three things that usually gate the outcome (SQL that is correct under messy joins, sound reasoning about experiments, and structured framing of an open-ended problem), candidates tend to over-invest in modelling theory and under-invest in framing.

Focus areas

Decompose a metric move by segment and mix
Pick a randomisation unit that respects interference
Separate novelty effects from durable behaviour change

Overview

Your work will directly impact users by providing them with tools to visualize and interpret geographic data, ultimately shaping how organizations understand their environments. The role is characterized by scale and complexity, as you will be working with large datasets and sophisticated algorithms to solve real-world problems. As part of a collaborative team, you will engage with various stakeholders, including product managers and software engineers, ensuring that the insights you generate lead to tangible business improvements.

The CARTO interview process

CARTO candidates report 2 rounds · ≈ 2-4 weeks. The stages below are what candidates describe, not a published process.

Screening Call

Most candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.

What to demonstrate

Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
The substance of the questions you ask back, which an experienced screener reads as a level signal

How to prepare

Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered

Technical Assignment

Before any modelling, the dataset is itself the first test. Take-home data usually carries something broken: rows duplicated at an unexpected grain, a join that silently drops part of the population, timestamps stored in more than one timezone, or missingness correlated with the outcome. An hour spent profiling row counts, key uniqueness and date ranges is not overhead, because it decides whether every number after it is real. What separates submissions is whether you report the defects you found and adapt the analysis to them, rather than modelling over them quietly and hoping the aggregate absorbs it.

What to demonstrate

Whether you established the grain of each table and checked row counts after every join, and said so in the writeup
Whether data defects you found are surfaced with their effect on the conclusion, instead of being dropped without comment
Whether filters and exclusions are reproducible from the submitted code, with the size of the excluded population quantified

How to prepare

Write a short profiling script you can point at any unfamiliar table: row count, distinct key count, null rate per column, and the min and max of every date field, then run it before anything else
Write the funnel or the join chain as one query and check the row count at each grain, so a silent fan-out shows up as a number rather than as a wrong answer later
On a past dataset, list every exclusion you applied and how many rows each one removed, then draft the single sentence about it you would put in a report

Where candidates lose points

Slicing a flat experiment until a segment reaches significance

Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.

Treating last-touch attribution as the causal value of a channel

The attribution label on dim_user is the output of a rule that assigns full credit to whichever touch happened to be recorded last inside a lookback window, and that rule systematically rewards channels that sit close to the conversion, especially branded search and retargeting, which largely intercept demand that already existed. Reallocating spend on those labels moves budget toward the channels that are best at being last, which is why attributed return on ad spend often improves while total signups do not. Nothing in the touchpoint data can settle this, because the counterfactual of not running the channel was never observed. The credible reads are a geo holdout or a scheduled pause, sized in advance on the total-signups metric rather than on the attributed one, and the honest framing in the meantime is that the label describes correlation with conversion and not incremental contribution.

Building features from data that postdates the prediction time

Check every feature against the timestamp at which the model would actually score, and drop anything computed from a window that includes or follows the label event. For a forecasting use case, split train and test by time rather than at random, and split by entity when the same entity recurs.

Treating a non-significant result as proof of no effect

Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.

Questions & practice

Describe your process for developing a predictive model from scratch.

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Set a baseline first, so any model has something honest to beat.

Follow-up

What would you monitor after launch to know the model is still valid?
How would you choose the decision threshold, and who owns that choice?

Describe a project where you used machine learning to solve a business problem.

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Check what information would not exist at prediction time, and exclude it.

Follow-up

What would you monitor after launch to know the model is still valid?
Where could label leakage enter this setup?

Simulate the false positive cost of repeated peeking

Medium · simulation · peeking · type i error

Quantify the cost of peeking. Simulate a two-arm experiment with no true effect: each arm accumulates Bernoulli conversions at a base rate of 0.10 up to 40,000 units per arm. Run a two-sided two-proportion z-test at alpha 0.05 at ten equally spaced interim points, and record whether the test ever crossed. Report the false positive rate over at least 10,000 replications, alongside the rate for a single look at the final sample only. Use a fixed seed and report a Monte Carlo standard error on both figures.

Approach

Generate each replication as two cumulative sums of Bernoulli draws, then read the interim points off the cumulative arrays. Regenerating data at each look would make the looks independent, which destroys exactly the dependence the exercise is about: later looks share data with earlier ones.
Use the pooled-variance two-proportion z: p_pool = (x1+x2)/(n1+n2), z = (p1-p2) / sqrt(p_pool*(1-p_pool)*(1/n1 + 1/n2)), reject when |z| > 1.96. State that the normal approximation is fine here because the smallest look has roughly 400 expected conversions per arm.
Vectorise across replications rather than looping: draw a (reps, n) array of uniforms, threshold at 0.10, cumsum along axis 1 and slice the ten look indices. A per-replication loop at 10,000 by 40,000 is unnecessarily slow.
Record the any-cross indicator per replication, take the mean, and compute the Monte Carlo standard error as sqrt(p*(1-p)/reps) so the reported figure comes with its own precision.
Report the single-look rate in the same run as a control. If it does not land near 0.05, the bug is in the test statistic and not in the peeking argument.

Worked solution (30 min)

rng = np.random.default_rng(seed); for memory, batch the replications in chunks and accumulate the any-cross count across chunks.
Per chunk: draw (chunk, 40000) uniforms per arm, x = (u < 0.10).cumsum(axis=1), slice columns at indices 3999, 7999, ..., 39999.
Compute the ten z statistics vectorised over the chunk, take crossed = (np.abs(z) > 1.96).any(axis=1).
Aggregate: peek_rate = total_crossed / reps; single_rate = mean of |z_final| > 1.96; mc_se = sqrt(p*(1-p)/reps) for each.

Expected result: The single-look rate lands at 0.05 within about 0.005 at 10,000 replications. The ten-look rate lands near 0.19, which with 10,000 replications has a Monte Carlo standard error of about 0.004, so anything in roughly 0.18 to 0.20 is consistent and anything near 0.40 indicates independent redraws.

Verify it yourself

The single-look control recovers 0.05 within two Monte Carlo standard errors; if it does not, the z statistic is wrong and the headline number is meaningless.
The crossing rate is monotone in the number of looks: 1 look near 0.05, 2 near 0.08, 5 near 0.14, 10 near 0.19.
Halving the base rate to 0.05 leaves the peeking rate essentially unchanged, since the result is about the test's random walk and not about the conversion level.
Re-running with the same seed reproduces the figure exactly.

Follow-up

Re-run with 40 looks instead of 10. Why does the curve flatten rather than continue rising linearly?
Among the replications that crossed, what is the mean observed lift, and why is it not zero?
What does an O'Brien-Fleming boundary or an always-valid confidence sequence change about this simulation, and what does each cost in power?

How would you optimize a SQL query to improve performance?

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

What breaks if events arrive late or out of order?
How would you verify this result without re-running the same query?

Find reactivation gaps in account paid-period history

Hard · gaps-and-islands · window-functions · subscriptions

fct_subscription_period holds account_id, subscription_id, period_start_utc, period_end_utc, period_status and change_reason. A mid-period plan or seat change closes one row and opens another, so a single continuous paid tenure is often many rows, and an account may hold two overlapping subscriptions. Collapse rows with period_status in ('active','past_due') into continuous tenures per account, treating gaps of three days or less as continuous. Return account_id, tenure_start, tenure_end, and for every tenure after the first, the gap in days that preceded it.

Approach

Filter to paid rows only: period_status IN ('active','past_due'). Trialing periods are not tenure, and including them turns every trial that never converted into a one-period tenure followed by a fake churn.
Order by period_start_utc and take a running maximum of all prior ends: MAX(period_end_utc) OVER (PARTITION BY account_id ORDER BY period_start_utc, period_end_utc, subscription_id ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING). Those three columns are the only stable ordering this schema exposes, so check first that they are unique within an account; if rows tie on all three, the island numbering is order-dependent between runs and you need a real row key before the result is reproducible.
LAG on its own is wrong here because with overlapping or nested periods the immediately preceding row by start date is not the one that ends latest, so the running maximum is the part that cannot be shortcut.
Flag a new island when prior_max_end IS NULL OR period_start_utc > prior_max_end + interval '3 days', then number islands with a running SUM of the flag over the same ordering and an explicit ROWS frame.
Group to (account_id, island) taking MIN(period_start_utc) and MAX(period_end_utc), then LAG(tenure_end) OVER (PARTITION BY account_id ORDER BY tenure_start) to compute the preceding gap in days for every tenure after the first.
Sanity-check with change_reason, which is the only lineage this schema carries: list its distinct values first, then confirm that rows recording a plan or seat change sit inside a tenure rather than opening one, and that every tenure after the first opens on a row whose reason records a restart rather than an ordinary renewal. Do not reconcile against a churn timestamp on dim_account, which this schema does not define; and where such a column does exist, a cancellation timestamp records when the request was made and routinely sits weeks before the period it ends.

Worked solution (35 min)

Find an account with a known mid-period upgrade and dump its period rows to use as the trace case.
Check that (period_start_utc, period_end_utc, subscription_id) is unique per account, since the whole ordering rests on it.
Write the paid-rows CTE and the running MAX with the explicit frame.
Add the island flag and the running SUM, then verify the trace account yields one island.
Group to tenures and add the LAG-based gap in days.
List the distinct change_reason values, then count accounts with more than one tenure and compare against the count of accounts carrying a restart-flavoured reason anywhere in their history.

Expected result: One row per (account_id, tenure). An account with a continuous history returns a single row with a NULL preceding gap. Every non-first tenure has gap_days strictly greater than 3 by construction.

Verify it yourself

No two tenures for the same account overlap or touch within the grace window.
MIN(tenure_start) per account equals MIN(period_start_utc) per account in the filtered input.
The account with a known mid-period upgrade produces one tenure, not two.
(period_start_utc, period_end_utc, subscription_id) has no duplicates within an account; if it does, say how many accounts are affected before reading the output.
The share of accounts with more than one tenure is small; if it is most accounts, the status filter or the grace window is wrong.

Follow-up

Why three days of grace? What do 0 and 30 days each do to the count of accounts classed as reactivated?
An account runs two concurrent subscriptions for different teams. One tenure or two, and what does the revenue reader expect?
How would you turn these tenures into a monthly gross logo churn series without double-counting an account that churned and returned in the same month?

How do you prioritize tasks when working on multiple projects?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
State what result would change your recommendation, so the answer is falsifiable.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
Which segment would you cut first, and what would that rule out?

Given a dataset with multiple features, how would you determine which features are most important for predicting outcomes?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

Which segment would you cut first, and what would that rule out?
How would you detect that the metric is being gamed rather than genuinely improving?

How would you approach a problem where the data is highly imbalanced?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Decompose the metric into the rates that drive it, and say which one you would check first.
State what result would change your recommendation, so the answer is falsifiable.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
What would you do if the primary metric and the guardrail moved in opposite directions?

Explain the difference between supervised and unsupervised learning.

Medium

Approach

Clarify what is being asked and what a complete answer would contain.
Work from the decision backwards to the evidence you would need.
State your assumptions explicitly before working the problem.

Follow-up

How would you know your answer was wrong?
What assumption would you test first?

Diagnose a 50.5 to 49.5 split before reading the result

Easy · sample ratio mismatch · exposure logging · chi-square

An experiment randomised on visitor_id ran for 11 days. fct_experiment_exposure holds 104,320 control rows and 102,180 treatment rows for the experiment_id, all with is_in_analysis_population = TRUE, against a configured 50/50 split. The treatment arm shows a 3.1% relative lift in visit-to-signup conversion at p = 0.004, and the team wants to ship. Decide whether that lift can be reported, show the arithmetic behind your decision, and name the three checks you would run first on the exposure logging.

Approach

Test the split instead of eyeballing it. Chi-square goodness of fit against 103,250 expected per arm gives 2 x 1070^2 / 103,250 = 22.2 on 1 degree of freedom, p about 2.5e-6. The conventional alarm threshold is p < 0.001, so this is a sample ratio mismatch.
Treat the mismatch as invalidating, not as a caveat. The arms are no longer exchangeable, so the 3.1% lift has no causal reading and rebalancing on covariates afterwards does not restore it.
Walk the logging path in the order units flow through it: assignment service, then the exposure event fired on the treated surface, then the filters that build the analysis population. Each stage can drop one arm preferentially.
Name the three suspects this stack actually has. Exposure emitted by client code the treatment arm reaches later or not at all when its bundle errors; an extra redirect or render in one arm that loses slow connections; and a post-assignment filter (is_bot_flagged, is_internal, consent_state) that correlates with variant.
Check whether the imbalance is uniform by splitting counts on session_date, surface, app_version and device_type. A mismatch confined to one client build or to day one points at a deployment fault that can be fixed and re-run, not at a design fault.

Worked solution (15 min)

N = 206,500, expected 103,250 per arm, observed deviation 1,070, which is 0.518% of the total pushed into one arm.
Chi-square = 2 x 1070^2 / 103,250 = 22.18 on 1 df, p about 2.5e-6.
Compare with the p < 0.001 threshold and stop: the readout is void.
Re-pull the same counts grouped by session_date and app_version to localise where the rows were lost.
Deliver one line: the split is off by 1,070 visitors, a roughly one-in-400,000 event under correct randomisation, so the experiment is being fixed and re-run rather than read.

Expected result: Chi-square(1) = 22.2, p about 2.5e-6, far past the p < 0.001 mismatch threshold. The correct deliverable is an exposure-logging investigation and a re-run, not a caveated 3.1% lift.

Verify it yourself

Cross-check the statistic as a z on one arm count: under a fair split that count has SD sqrt(N/4) = sqrt(51,625) = 227 visitors, so the 1,070 deviation is 4.71 SD, and 4.71^2 = 22.2 reproduces the chi-square.
Chi-square scales linearly with N at a fixed ratio, so the same 50.518% split at N = 2,000 returns 22.18 / 103.25 = 0.21, p = 0.64. That reconciles the arithmetic and nothing more; it is not evidence that the alarm is independent of N. What rules out the large N is that the p-value is uniform under correct randomisation at every sample size, so p = 2.5e-6 is a one-in-400,000 draw whether N is 2,000 or 2,000,000. Large N buys the power to resolve a 0.5% skew; it does not manufacture one.
The mismatch p-value should be orders of magnitude smaller than the lift's p-value; if they are comparable, recheck the arithmetic.
Arm counts must sum to the total exposure rows for that experiment_id. If they do not, the analysis-population filter is already dropping rows before you looked.

Follow-up

The imbalance is entirely in one app_version on android. Can you salvage the other surfaces, and under what argument?
How would you monitor this across every running experiment without drowning in false alarms from a daily chi-square at alpha 0.05?
The missing rows turn out to be disproportionately visitors with consent_state = 'unknown'. What does that do to the estimate even after the logging bug is fixed?

Size every candidate cause of a trial-to-paid decline

Hard · cohort-maturity · experiment-readout · mix-shift · contribution-sizing

Trial-to-paid conversion on weekly trial-start cohorts from fct_subscription_period reads 3.1 points below the trailing eight-week mean for the three most recent cohorts. Three things happened in that window: a pricing experiment reached 50% of new trials, a payment processor migration added settlement delay, and paid_search spend tripled. Using fct_subscription_period, fct_experiment_exposure and dim_user, rank the causes by their contribution in points of the headline, state the remainder, and give the decision you would take on Monday.

Approach

Kill the immature cohorts first, because everything downstream is computed on them. The metric is lagged by the trial length plus a 14-day conversion window plus a settlement allowance, and a processor migration lengthens exactly that last term; recompute each cohort at a fixed cohort age rather than as of today, and confirm the newest cohort's value is still climbing day over day.
Hold the experiment analysis to the exposed population. Join fct_experiment_exposure on unit_id with is_in_analysis_population = TRUE rather than reading an assignment log, then check the variant split for a sample-ratio mismatch before believing any effect at all. Contribution to the headline is the variant effect multiplied by the exposed share, which is not the same number as the variant effect.
Decompose the cohort mix by dim_user.first_touch_channel using the same weight-times-rate arithmetic as any other mix question, so the paid_search increase is sized as a weight change at a measured conversion rate rather than asserted from the spend figure.
Convert all three to points of the headline, sum them, and print the residual against the historical week-to-week standard deviation of the metric. If the residual is inside that band, say so and stop looking; if it is outside, name what you would investigate next rather than leaving it implied.
Land the decision. Only one of the three is actionable on Monday, so state whether the experiment has accrued enough exposed units to stop at the pre-declared horizon, and state separately what the settlement-lag correction does to the published series and its lag rule.

Follow-up

How do you choose the fixed cohort age, and what do you lose by choosing it too long?
If the pricing variant is genuinely 1.2 points worse, does that settle whether to stop it? What else is on the other side of that decision?
The trailing eight-week mean spans the processor migration. What is the right baseline instead?

Suggested practice checklist

For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.

Day 1 — Design one test end to end on paper

Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
State in advance what you will do if the primary metric is flat while a secondary metric is significant.

Deliverable: A one-page test design with a decision rule written before launch.

Day 2 — Power arithmetic until it is automatic

Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.

Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.

Day 3 — Variance and the unit-of-analysis problem

Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.

Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.

Day 4 — Validity threats you can actually test for

Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.

Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.

Day 5 — When randomization is not available

Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.

Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.

Day 6 — The readout query

Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.

Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.

Day 7 — Present it to someone who will not read the appendix

Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
Rewrite your opening line so the recommendation lands before any methodology.

Deliverable: A one-page readout whose first line is the recommendation.

Stories where someone disagreed with you

Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.

Describe a challenging situation you faced in a team environment and how you resolved it.

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
Close with what you would do differently, concretely.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

What would you do differently if you ran that project again?
How did you know the outcome was caused by your change?

Tell me about a time when you had to persuade a team member to adopt your analysis.

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Name the disagreement or constraint, and how you resolved it with evidence.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

What would you do differently if you ran that project again?
How did you know the outcome was caused by your change?

Walk through an analysis you got wrong and what changed

Easy · postmortem · data quality · self-assessment

Describe an analysis of yours that turned out to be wrong after somebody had already acted on it. You have four minutes. The account must name the defect mechanically, the join, the filter, the window or the identity key, rather than describing it as a communication problem. It must also say who did what because of the wrong number, how the error surfaced, how long it stood, and what control you put in place so that class of error cannot reach a decision again. Do not pick an error nobody acted on.

Approach

Recognise what is being probed: whether you can be specific about your own failure without minimising it or performing contrition. The discriminator is whether the defect has a mechanism the listener could reproduce in their own warehouse.
Choose the case by blast radius rather than by comfort. An error nobody acted on tests nothing, and picking one signals that you are managing the interview instead of answering it.
Structure the account in six beats: the number, the decision it drove, the defect, the detection, the correction, the control. Keep the defect to one reproducible sentence, for example an inner join to fct_subscription_period that dropped accounts with no subscription row and so computed retention over payers only.
State the direction of the bias, not only its existence. A filter or join that removes rows usually moves a metric predictably, and knowing which way shows you diagnosed the mechanism rather than patched the symptom.
Be exact about detection and elapsed time. 'A colleague noticed' and 'the row-count assertion failed before publication' are different answers about the same organisation, and the second one is the one your control is supposed to produce next time.
End on the control, its cost, whether it has fired since, and one thing it does not cover.

Follow-up

What did the control cost, and has it fired since? If it never has, how do you know it works?
How long did the wrong number stand before anyone questioned it, and what does that say about the review path it went through?
What is the equivalent mistake you are most likely to make in this role, given the tables you would be working in?