MedeAnalytics Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at MedeAnalytics, you will play a pivotal role in shaping the technology solutions that drive our data analytics platform. This role is essential not only for the development of software applications but also for ensuring that our products are robust, scalable, and tailored to meet the needs of our diverse clientele. Your contributions will directly impact the quality of data insights that healthcare organizations rely on to improve patient outcomes and operational efficiency. In this role, you will work on complex problems involving large datasets, and you will collaborate with cross-functional teams to create innovative solutions. Your work will involve designing software that enhances data visualization and analytics capabilities, which are critical in the healthcare industry.

This guide is scoped to a Software Engineer candidate at MedeAnalytics.

Focus areas

• SQL
• SQL JOINs
• SQL SELECT queries

The MedeAnalytics interview process

MedeAnalytics candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening Call

A preliminary call to assess the candidate's background and fit for the role.

What to demonstrate

• A preliminary call to assess the candidate's background and fit for the role
• Depth in SQL

How to prepare

• Be able to walk your CV end to end in two minutes, and say why this company specifically.
• Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Technical Interviews

Multiple rounds of interviews focusing on technical skills and problem-solving abilities.

What to demonstrate

• Multiple rounds of interviews focusing on technical skills and problem-solving abilities
• Depth in SQL

How to prepare

• Answer aloud and timed: Explain the differences between various data structures (e.g., arrays, lists, dictionaries).
• Answer aloud and timed: Write a function to reverse a string.

Behavioral Interviews

Interviews designed to evaluate interpersonal skills and cultural fit within MedeAnalytics.

What to demonstrate

• Interviews designed to evaluate interpersonal skills and cultural fit within MedeAnalytics
• Depth in SQL

How to prepare

• Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
• Re-read the description of the behavioral interviews above and write down what you would ask to confirm before it.

Final Interviews

Concluding interviews that may include practical exercises and discussions on team collaboration.

What to demonstrate

• Concluding interviews that may include practical exercises and discussions on team collaboration
• Depth in SQL

How to prepare

• Answer aloud and timed: How would you approach debugging a large codebase?
• Answer aloud and timed: Describe a time when you had to troubleshoot a system failure.

Where candidates lose points

Prepare for coding challenges

Make sure to practice solving algorithmic problems and familiarize yourself with coding platforms like LeetCode or HackerRank.

Understand the company’s mission

Familiarize yourself with MedeAnalytics's work in healthcare analytics and how your role contributes to improving patient outcomes.

Ask insightful questions

Prepare thoughtful questions to ask your interviewers, demonstrating your interest in the company and role.

Questions & practice

Given a list of numbers, how would you find the two numbers that add up to a specific target?

Medium · Problem-Solving Questions

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

1. Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
2. Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
3. State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
4. Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

• The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
• A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

1. Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
2. For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
3. For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
4. Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

• A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
• Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

Describe how you would optimize a SQL query for performance.

Medium · Technical Questions

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. Handle the rows that do not match: that is usually the actual question.

Follow-up

• How does the query change if that join becomes one-to-many?
• What happens to this when the table is ten times larger?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

1. Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
2. Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
3. Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
4. Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

• Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
• Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

Explain the differences between various data structures (e.g., arrays, lists, dictionaries).

Medium · Technical Questions

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

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

Write a function to reverse a string.

Medium · Technical Questions

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

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

How do you ensure the security of your applications?

Medium · Technical Questions

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

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

How would you approach debugging a large codebase?

Medium · Problem-Solving Questions

Approach

1. Establish what changed and when, before forming any theory.
2. Pick a bisection that eliminates candidates whichever way it turns out.
3. Check the instrumentation before believing the symptom.
4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

• What would you look at first, and what would it rule out?
• How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics MedeAnalytics candidates report.

Day 1 — Map the MedeAnalytics loop

• Write out the reported sequence: Initial Screening Call, Technical Interviews, Behavioral Interviews, Final Interviews.
• For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work SQL

• Spend the session on SQL, which MedeAnalytics candidates report being tested on.
• Write one worked example in SQL and time yourself on it.

Deliverable: One timed worked example in SQL.

Day 3 — Work SQL JOINs

• Spend the session on SQL JOINs, which MedeAnalytics candidates report being tested on.
• Write one worked example in SQL JOINs and time yourself on it.

Deliverable: One timed worked example in SQL JOINs.

Day 4 — Work SQL SELECT queries

• Spend the session on SQL SELECT queries, which MedeAnalytics candidates report being tested on.
• Write one worked example in SQL SELECT queries and time yourself on it.

Deliverable: One timed worked example in SQL SELECT queries.

Day 5 — Answer out loud: Technical Questions

• Answer aloud, timed: Describe how you would optimize a SQL query for performance.
• Answer aloud, timed: How do you handle exceptions in your code?

Deliverable: Spoken answers to 2 reported Technical Questions question(s), under time.

Day 6 — Answer out loud: Problem-Solving Questions

• Answer aloud, timed: Given a list of numbers, how would you find the two numbers that add up to a specific target?
• Answer aloud, timed: How would you approach debugging a large codebase?

Deliverable: Spoken answers to 2 reported Problem-Solving Questions question(s), under time.

Day 7 — Answer out loud: Behavioral Questions

• Answer aloud, timed: Tell me about a time you had to collaborate with a difficult team member.
• Answer aloud, timed: How do you prioritize your tasks when you have multiple deadlines?

Deliverable: Spoken answers to 2 reported Behavioral Questions question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

How do you handle exceptions in your code?

Medium · Technical Questions

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a time when you had to troubleshoot a system failure.

Medium · Problem-Solving Questions

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Tell me about a time you had to collaborate with a difficult team member.

Medium · Behavioral Questions

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you prioritize your tasks when you have multiple deadlines?

Medium · Behavioral Questions

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a situation where you took the lead on a project.

Medium · Behavioral Questions

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?