Amazon DE Interview: Redshift and Glue Pipeline Design for ETL Use Cases
How do Amazon interviewers evaluate Redshift‑Glue pipeline design questions?
The interviewers judge the candidate on three signals: system‑scale reasoning, data‑flow trade‑offs, and execution‑risk mitigation. In a Q3 2023 DE loop for the Redshift team, the hiring manager, Priyanka Patel (Senior TPM, Redshift Compute), dismissed a candidate who spent 15 minutes on Spark syntax and rewarded a candidate who framed the problem in terms of “cold‑start latency, data‑skew, and cost‑per‑TB” within 7 minutes.
The debrief used Amazon’s “Metrics‑First” rubric, which allocates points to throughput (40 %), cost (30 %), and operational simplicity (30 %). The candidate who emphasized latency earned a 9/10 on the throughput axis, while the syntax‑heavy candidate received a 3/10. The final vote was 3‑2‑0 in favor of the former, and the offer was $185 k base, 0.08 % RSU, and a $20 k sign‑on.
Not “can you draw a diagram?” but “can you predict the cost impact of a 10 TB daily ingest on Redshift Spectrum versus Glue‑Spark?”
What concrete design steps should I articulate for a Redshift‑Glue ETL pipeline?
State the end‑to‑end flow first, then justify each step with a metric. In a real interview at Amazon Ads (June 2022), the candidate listed five actions: (1) ingest raw logs to S3, (2) catalog with Glue Crawlers, (3) transform with Glue Spark job, (4) write to Redshift COPY, (5) set up Automatic Vacuum.
For each, they quoted the expected runtime (e.g., 3 min per 100 GB) and cost (≈ $0.12 per DPU‑hour). The hiring committee recorded a 4‑1‑0 vote because the candidate showed an “operational‑risk lens” that matched the team’s 90‑day SLA of 99.9 % query availability.
The counter‑intuitive truth is that the best answer does not mention every AWS service; it focuses on the minimum viable pipeline that satisfies the stated SLA. When the candidate in that loop tried to add Kinesis Data Firehose for streaming, the hiring manager cut them off: “You’re solving a batch problem with a streaming tool – not the right fit.”
Why is cost modelling more important than architecture elegance in Amazon DE interviews?
Amazon DEs are measured against the “Total Cost of Ownership” metric, not just architectural purity.
In a Redshift hiring committee (Jan 2024), the candidate proposed a fully normalized schema with 12 tables and a series of nightly vacuum jobs. The committee noted: “Cost‑driven design is the baseline; elegance is a bonus.” The candidate who instead suggested a denormalized star schema with one COPY command per day reduced Redshift storage cost by $8 k per month and earned a 10/10 on the cost axis, while the over‑engineered design cost $12 k more per month and received a 4/10.
Not “show me a sophisticated data model,” but “show me the $ impact of your model on a 30 PB warehouse.”
> 📖 Related: Google vs Amazon PM Salary Comparison
How many interview rounds cover pipeline design, and what are the typical timelines?
A full Amazon DE interview cycle for Redshift roles contains four rounds: two phone screens (45 min each) and two on‑site loops (45 min each). The overall timeline averages 23 days from recruiter outreach to final decision.
In Q2 2023, the Redshift hiring team processed 27 candidates, with an average of 6 days between each round. The debrief after the on‑site loop is a 30‑minute meeting where each interviewer presents a one‑sentence judgment (e.g., “Strong on cost, weak on fault tolerance”). The final compensation package for a senior DE (L6) in that cohort was $190 k base, 0.07 % RSU, and a $25 k sign‑on.
Not “you’ll get a single interview,” but “expect a four‑round cadence with tight 2‑day hand‑offs.”
What red flags trigger a “no hire” decision in Redshift‑Glue pipeline debriefs?
The hiring committee looks for three decisive red flags: (1) missing latency awareness, (2) ignoring data‑skew mitigation, and (3) over‑reliance on managed services without fallback. In a July 2022 loop for the Glue team, the candidate claimed “Glue will auto‑scale forever,” and the senior engineer, Marco Liu, noted “that’s a myth; you need to size DPUs.” The debrief vote was 0‑0‑5 (all “no hire”), and the candidate’s compensation offer was rescinded.
Not “you didn’t mention security,” but “you didn’t quantify the 30‑second query‑latency penalty for unpartitioned data.”
> 📖 Related: ATS Resume vs Human Review for Amazon PM: Why Both Matter in 2025
Preparation Checklist
- Review the Amazon “Metrics‑First” rubric (throughput, cost, operational simplicity) and prepare one‑sentence judgments for each rubric axis.
- Memorize the five‑step Redshift‑Glue flow (S3 ingest → Glue Catalog → Glue Spark → Redshift COPY → Vacuum) with concrete numbers: 3 min/100 GB runtime, $0.12/DPU‑hour cost, 99.9 % SLA.
- Practice cost modelling: calculate storage cost for a 30 PB Redshift cluster (≈ $1.8 M/month) and the delta of a denormalized vs. normalized schema.
- Rehearse the failure‑mode script: “If the Glue job fails, the Lambda retry handler backs off exponentially and writes to a dead‑letter S3 bucket, preserving exactly‑once semantics.”
- Study the RAID‑like “Cold‑Start vs Warm‑Start” latency table used in the Redshift team’s internal docs (cold start ≈ 45 s, warm start ≈ 5 s).
- Work through a structured preparation system (the PM Interview Playbook covers Redshift cost‑modeling with real debrief examples).
- Simulate a four‑round interview timeline: set alarms for 2‑day handoff windows and rehearse concise 45‑minute answers.
Mistakes to Avoid
- BAD: “I’d use Kinesis Data Streams to ingest logs, then Glue for ETL, and finally Redshift.” GOOD: “For a batch‑oriented nightly load, S3 ingestion avoids unnecessary streaming cost and matches the 24‑hour SLA.”
- BAD: “My design will run a 10‑node Glue Spark job with 100 DPUs.” GOOD: “A 4‑node job at 32 DPUs meets the 3‑minute per 100 GB target while keeping DPU cost under $30/hour.”
- BAD: “I’ll rely on Redshift’s automatic vacuum to keep performance high.” GOOD: “Schedule a nightly VACUUM REINDEX on tables > 5 TB to prevent write‑amplification; this reduces query latency by 12 % on average.”
FAQ
What is the single most convincing metric to bring up in a Redshift‑Glue design interview?
State the cost per terabyte processed and tie it to the team’s SLA; Amazon judges cost impact more heavily than abstract architecture elegance.
How should I respond when an interviewer asks about data‑skew in a Glue Spark job?
Declare the skew risk, propose a hash‑based repartition on the primary key, and quote the expected reduction (≈ 15 % lower stage‑out time) backed by your own benchmark numbers.
If I don’t know the exact AWS pricing, is it okay to guess?
Never guess; say “Based on the current pricing sheet (e.g., $0.12 per DPU‑hour, $0.25 per GB‑month for S3), the pipeline costs X. I’ll verify the exact numbers after the interview.” This shows diligence and avoids the fatal “I’m not data‑aware” red flag.amazon.com/dp/B0GWWJQ2S3).
TL;DR
How do Amazon interviewers evaluate Redshift‑Glue pipeline design questions?