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Tenaska Software Engineer Interview Guide

Tenaska Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at Tenaska, you play a vital role in building and maintaining the technological infrastructure that supports one of the leading independent energy companies in the United States. Your work directly impacts how energy trading, project engineering, and operational systems function at scale. By developing robust applications and optimizing data pipelines, you empower business units to make fast, informed decisions in a fast-paced market. The role combines technical execution with a strong emphasis on collaboration, as you will work closely with traders, high-level engineers, and operational teams. You will tackle complex problems involving automation, continuous integration and continuous deployment pipelines, and system reliability.

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

Focus areas

  • CI/CD Pipelines
  • Transmission Planning
  • System Operator / Power Systems Operations

The Tenaska interview process

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

Initial Screening

The first step involves an initial assessment to gauge your qualifications and fit for the role.

What to demonstrate

  • The first step involves an initial assessment to gauge your qualifications and fit for the role
  • Depth in CI/CD Pipelines

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

Candidates will undergo technical assessments to evaluate their skills and problem-solving abilities.

What to demonstrate

  • Candidates will undergo technical assessments to evaluate their skills and problem-solving abilities
  • Depth in CI/CD Pipelines

How to prepare

  • Answer aloud and timed: 1–2 sentences introducing the category and what it tests.
  • Answer aloud and timed: Bullet list of realistic example questions: Tell me about a time you had to collaborate with a non-technical stakeholder to deliver a project. How do you handle disagreements on technical direction within a team? What interests you about working in the energy sector and specifically at Tenaska? Describe a situation where a project failed or went off track, and explain how you handled it.

Behavioral Assessments

Final interviews focus on behavioral aspects to understand how candidates interact and collaborate with team members.

What to demonstrate

  • Final interviews focus on behavioral aspects to understand how candidates interact and collaborate with team members
  • Depth in CI/CD Pipelines

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 assessments above and write down what you would ask to confirm before it.

Where candidates lose points

Know your resume

Expect interviewers to pick apart every project, tool, and pipeline you mention, so be ready to defend your architectural decisions.

Show curiosity about the business

Take time to understand how energy trading and operational engineering intersect at Tenaska to ask insightful questions during your panel.

Be transparent about challenges

When discussing past failures or production bugs, focus on what you learned and how you improved your processes moving forward.

Questions & practice

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

  1. Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
  2. Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
  3. Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
  4. Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

  • A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
  • Where does the fingerprint get computed relative to request decompression and the body-size limit?

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?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

  1. Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
  2. Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
  3. State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
  4. Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

  • The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
  • Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

  1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
  2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
  3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
  4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

  • The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
  • How do you verify the backfill actually finished, given rows are still being inserted while it runs?

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?

1–2 sentences introducing the category and what it tests.

Medium · Technical and Experience Deep Dives

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?

1–2 sentences introducing the category and what it tests.

Medium · Situational and Problem-Solving

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?

Bullet list of realistic example questions: How do you approach troubleshooting an unfamiliar system when docu

Medium · Situational and Problem-Solving

Bullet list of realistic example questions: How do you approach troubleshooting an unfamiliar system when documentation is sparse? If a deployment fails in a production environment, what is your immediate step-by-step containment strategy? How do you prioritize your daily workload when multiple business teams request features simultaneously?

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 Tenaska candidates report.

Day 1 — Map the Tenaska loop

  • Write out the reported sequence: Initial Screening, Technical Interviews, Behavioral Assessments.
  • 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 3 reported rounds, with the weakest marked.

Day 2 — Work CI/CD Pipelines

  • Spend the session on CI/CD Pipelines, which Tenaska candidates report being tested on.
  • Write one worked example in CI/CD Pipelines and time yourself on it.

Deliverable: One timed worked example in CI/CD Pipelines.

Day 3 — Work Transmission Planning

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

Deliverable: One timed worked example in Transmission Planning.

Day 4 — Work System Operator / Power Systems Operations

  • Spend the session on System Operator / Power Systems Operations, which Tenaska candidates report being tested on.
  • Write one worked example in System Operator / Power Systems Operations and time yourself on it.

Deliverable: One timed worked example in System Operator / Power Systems Operations.

Day 5 — Answer out loud: Technical and Experience Deep Dives

  • Answer aloud, timed: 1–2 sentences introducing the category and what it tests.
  • Answer aloud, timed: Bullet list of realistic example questions: Can you walk us through your experience building and managing CI/CD pipelines? What specific technologies and frameworks have you used in your past projects to handle data processing? How do you ensure code quality and maintainability when working under tight deadlines? Describe a complex technical problem you solved in a previous role and the tools you used to diagnose it.

Deliverable: Spoken answers to 2 reported Technical and Experience Deep Dives question(s), under time.

Day 6 — Answer out loud: Behavioral and Culture Fit

  • Answer aloud, timed: 1–2 sentences introducing the category and what it tests.
  • Answer aloud, timed: Bullet list of realistic example questions: Tell me about a time you had to collaborate with a non-technical stakeholder to deliver a project. How do you handle disagreements on technical direction within a team? What interests you about working in the energy sector and specifically at Tenaska? Describe a situation where a project failed or went off track, and explain how you handled it.

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

Day 7 — Answer out loud: Situational and Problem-Solving

  • Answer aloud, timed: 1–2 sentences introducing the category and what it tests.
  • Answer aloud, timed: Bullet list of realistic example questions: How do you approach troubleshooting an unfamiliar system when documentation is sparse? If a deployment fails in a production environment, what is your immediate step-by-step containment strategy? How do you prioritize your daily workload when multiple business teams request features simultaneously?

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

Behavioural preparation

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

Bullet list of realistic example questions: Can you walk us through your experience building and managing CI/C

Medium · Technical and Experience Deep Dives

Bullet list of realistic example questions: Can you walk us through your experience building and managing CI/CD pipelines? What specific technologies and frameworks have you used in your past projects to handle data processing? How do you ensure code quality and maintainability when working under tight deadlines? Describe a complex technical problem you solved in a previous role and the tools you used to diagnose it.

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?

1–2 sentences introducing the category and what it tests.

Medium · Behavioral and Culture Fit

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?

Bullet list of realistic example questions: Tell me about a time you had to collaborate with a non-technical s

Medium · Behavioral and Culture Fit

Bullet list of realistic example questions: Tell me about a time you had to collaborate with a non-technical stakeholder to deliver a project. How do you handle disagreements on technical direction within a team? What interests you about working in the energy sector and specifically at Tenaska? Describe a situation where a project failed or went off track, and explain how you handled it.

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?