Roku Data Engineer Interview: Questions & Prep (2026)

Roku is a US-based streaming technology company known for its smart TV operating system, the Roku Channel, and its advertising platform. Data Engineers at Roku build pipelines that process billions of streaming events, power ad measurement, and feed recommendation systems. With 253 open roles currently listed on knok jobradar, Roku is one of the more active data hirers right now.

Candidates report a process that typically includes a recruiter screen, one or two technical rounds covering SQL and Python or Spark, a system design session focused on pipeline architecture, and a final behavioural panel. Roku does not publish official round names, so treat every stage as high-stakes. Rounds are conducted over video call.

Salary bands for Data Engineers based on knok jobradar data:

Context on competition: 542 Data Engineer roles are open across India right now, with Bangalore leading at 92 openings and Delhi at 66.

These questions come up repeatedly in Roku Data Engineer interviews, based on what candidates report and the nature of Roku's data stack (ad events, streaming telemetry, and recommendation pipelines).

1. Walk me through how you would design a real-time pipeline to process Roku's streaming event data at scale.
2. Roku handles ad impressions at high volume. How would you ensure exactly-once delivery in a Kafka-based pipeline?
3. A data quality issue in your pipeline causes incorrect ad revenue numbers. How do you diagnose and remediate it?
4. How have you optimised a slow Spark job? What was the root cause and what changes did you make?
5. Explain how you would model a slowly changing dimension (SCD Type 2) for Roku's device inventory table.
6. How would you design a data lake schema to serve both ad-hoc analyst queries and scheduled ML feature pipelines without conflicts?
7. Describe your experience with columnar formats like Parquet or ORC. When do you choose one over the other?
8. How do you handle late-arriving events in a streaming pipeline that powers a real-time dashboard?
9. A downstream team says their query broke after your schema migration. How do you manage schema evolution without breaking consumers?
10. How would you implement data lineage tracking across a multi-hop pipeline that touches raw, curated, and serving layers?
11. Roku runs both batch and streaming workloads. How do you decide which architecture fits a new use case?
12. Describe a time you worked with analysts, product managers, or ML engineers to deliver a shared data product under a tight deadline.

Q: How have you optimised a slow Spark job?

*Situation:* At my previous company, our nightly user engagement aggregation job was taking well over four hours to complete, causing the morning dashboard to be stale when business teams arrived.

*Task:* I was asked to cut the runtime significantly without changing the output schema or adding hardware.

*Action:* I profiled the job using the Spark UI and found two problems: a large shuffle caused by joining two unevenly distributed datasets, and repeated reads of the same raw Parquet files across stages. I rewrote the join to broadcast the smaller lookup table, repartitioned the larger dataset on the join key before writing, and cached intermediate DataFrames that were reused downstream. I also switched the output format from CSV to Parquet with Snappy compression to speed up subsequent reads.

*Result:* Runtime dropped by more than half. The morning dashboard was populated before business hours, storage costs fell because compressed output was smaller, and the approach I documented became the team's standard checklist for new Spark jobs.

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Q: Describe a time a data quality issue caused incorrect reporting.

*Situation:* Our ad revenue dashboard showed a sharp overnight drop. Finance escalated immediately, worried it reflected a real decline in ad spend.

*Task:* I had to determine whether this was a pipeline bug or an actual business event, and fix it fast if it was a pipeline issue.

*Action:* I checked raw event counts in our Kafka topic first and found they were normal, so the problem was downstream. Tracing the lineage step by step, I found that a schema change in an upstream service had introduced a nullable field that my pipeline treated as zero instead of skipping the record. I patched the null handling, added a row-count reconciliation check between source and target, and backfilled the affected partition.

*Result:* Revenue numbers were corrected within two hours. I then added automated data quality checks at every pipeline stage, which caught two similar issues in the following quarter before they reached reporting.

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Q: Tell me about working with a cross-functional team to deliver a data product.

*Situation:* Our ML team needed a feature store for a new content recommendation model, but the raw events they needed were owned by the data engineering team and had never been cleaned or documented.

*Task:* I was the data engineer assigned to partner with the ML team and deliver production-ready features within six weeks.

*Action:* I ran a joint session with the ML engineers and a product analyst to agree on exact feature definitions and acceptable data freshness. I then built incremental Spark jobs to compute the features daily, stored them in a partitioned Hive table with a versioned schema, and wrote a README documenting every feature's business logic and known edge cases. I also set up alerts for pipeline failures so the ML team would know before running training jobs.

*Result:* The feature store went live on schedule and supported three subsequent model experiments without any pipeline changes. The ML team reported that clear documentation cut their onboarding time for new features significantly.

For system design questions (pipelines and architecture):
Start by clarifying requirements: data volume, latency needs (real-time vs. batch), consistency guarantees, and downstream consumers. Then describe the ingestion layer, processing layer, storage layer, and serving layer in order. State trade-offs explicitly (for example, 'I would choose Kafka over SQS here because the partition model suits our throughput'). Roku interviewers typically want to see that you think about failure modes, schema evolution, and monitoring from the start, not as afterthoughts.

For SQL and coding questions:
Think out loud before writing. Confirm the table schema and expected output. Write a correct solution first, then optimise. For window functions, name the window and explain what it partitions. If asked about performance, mention partitioning, predicate pushdown, and avoiding full table scans.

For behavioural questions (STAR method):
Keep the Situation and Task brief (one or two sentences each). Spend most of your time on the Action: what you personally did, the decisions you made, and why. The Result should be specific and, where possible, quantified. If you cannot share exact numbers due to confidentiality, say so and give a relative improvement.

For debugging and data quality questions:
Follow a structured path: check whether the problem sits in the source data, the transformation logic, or the loading step. Show that you rely on monitoring and alerting rather than just manual investigation. Always mention how you would prevent the same issue from recurring.

Roku data teams typically look for three things beyond raw technical skill.

Ownership mindset. Candidates who treat the pipeline as their product, not just their code, stand out. This means proactively monitoring, handling failures gracefully, and thinking about downstream impact before making changes. Interviewers probe for this by asking how you handled a pipeline that broke and what you did to prevent recurrence.

Fluency with scale. Roku processes streaming data from millions of devices. Interviewers want evidence that you have worked with, or have thought carefully about, high-volume event data, partitioning strategies, and distributed compute. Answers that only reference small-scale side projects rarely satisfy this bar.

Clear communication with non-technical partners. Data Engineers at Roku collaborate closely with product managers, analysts, and ML engineers. Interviewers commonly ask for situations where you had to explain a complex technical decision or push back on an unrealistic data request. Show that you can translate between engineering and business language without losing precision.

Week 1: Core technical foundations
Revise window functions, CTEs, and query optimisation in SQL. Practice Spark fundamentals: transformations vs. actions, shuffle operations, broadcast joins, and caching. Review Kafka concepts: partitions, consumer groups, offsets, and exactly-once semantics.

Week 2: System design for data
Practice designing three types of systems: a real-time event pipeline (think ad impression tracking), a batch aggregation pipeline (think daily reporting), and a data lake with multiple consumer types. For each, articulate the trade-offs between tools (Spark vs. Flink, Parquet vs. Delta Lake, Airflow vs. Prefect).

Week 3: Roku-specific context and behavioural prep
Read publicly available material about Roku's advertising platform and how streaming analytics works at device scale. Prepare five to six STAR stories covering: a complex debugging incident, a cross-team collaboration, a time you improved pipeline reliability, a time you pushed back on a poor technical decision, and a time you learned something new quickly.

Week 4: Mock interviews and tightening
Do at least two timed SQL mock sessions and one full system design mock with a peer or mentor. Record yourself answering behavioural questions and check that your answers are concise (under three minutes each) and specific, not generic.

Jumping to a solution without clarifying requirements. In system design rounds, candidates who start drawing architecture before asking about scale and latency almost always miss key constraints. Spend the first two or three minutes asking questions.

Writing SQL that works but ignores performance. A correct query that would time out on a very large table shows limited production experience. Always mention partitioning, filtering early, and avoiding SELECT *.

Generic STAR answers. Saying 'we improved the pipeline' without explaining what you personally did and what changed measurably is a red flag. Interviewers are assessing your individual contribution, not your team's.

Ignoring failure modes in pipeline design. A design that only covers the happy path will be challenged. Always address what happens when a source is late, a schema changes, or a job fails halfway through.

Not knowing Roku's business context. Roku earns the majority of its revenue from advertising, not hardware. Candidates who do not understand this often give generic streaming answers instead of ad-tech-informed ones. Know the difference between device telemetry pipelines and ad measurement pipelines.

Weak monitoring and alerting plans. A pipeline without observability is a pipeline waiting to fail silently. Mention alerting, row-count checks, and SLAs in every design you present.