Goldman Sachs Senior Data Engineer Interview (L5)
Hiring for Senior Data Engineer at Goldman Sachs (L5) runs Investment-bank rigor with Marcus/transaction-banking modernization and strats/quant culture. The hiring bar is independent technical leadership and cross-team influence; the median candidate brings 5-8 years of DE experience.
Compensation
$190K–$240K base • $350K–$500K total
Loop duration
4 hours onsite
Rounds
5 rounds
Location
New York, Dallas, Salt Lake City, London, Bangalore
Practice problems
Goldman Sachs senior data engineer practice set
Problems the Goldman Sachs senior data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.
The Holdouts
In our push-notification log, each message records the plan tier it targeted in the `platform` field. Find the unique users who were sent a `basic`-tier notification but never a `premium`-tier one.
The Overlap
Your monitoring system logs server maintenance as `[start, end]` minute ranges, and windows that overlap or sit back-to-back really describe one continuous outage. Collapse the `windows` so any that overlap or touch at an endpoint become a single range, and return them ordered by start time. Two windows touch when one ends exactly where the next begins.
Letters in the Noise
A text-cleaning step in your pipeline needs a per-letter tally of the raw strings passing through it. For a given `s`, return the letter frequencies as `[letter, count]` pairs.
Top 2 sellers by revenue in each marketplace
Classic DE round opener. Window function + partition. Edit to tweak the threshold.
Walk into Goldman Sachs knowing the system design pattern they'll test.
Two Years of Every Click
Every click, every aisle, every day for two years.
Pulled from debriefs where system design separated levels.
The loop
How the interview actually runs
01Recruiter screen
30 minGoldman is formal, traditional, and selective. DE hiring spans Engineering (platform), Strategists (quant-adjacent), and Marcus (consumer tech). Tracks differ materially.
- →Strats roles blend quant + engineering; coding interviews can be harder
- →Marcus is a modern tech stack inside a traditional bank
- →Dress formally; tone formally; Goldman is not casual
02Technical phone screen
60 minSQL + coding with finance-data flavor. Trade data, position reconciliation, risk calculations. Slang is specific; familiarize.
- →Trading-floor vocabulary: ticker, cusip, side (buy/sell), settlement date
- →SQL performance questions are common; Goldman cares about cost
- →Python round can cover OO design for financial models
03Onsite: data architecture
60 minDesign a system supporting trading analytics, regulatory reporting (Dodd-Frank, MiFID II), or consumer banking analytics.
- →Regulatory reporting has extreme correctness requirements
- →Trading data has unique latency + consistency demands
- →Goldman's SecDB is the famous internal system; familiarity is a plus
04System design (pipeline architecture)
60 minDesign a production pipeline end-to-end: ingestion, transformation, storage, consumers, SLAs, failure modes, backfill strategy, and cost trade-offs. At senior level, you drive the conversation without prompting. Expect follow-ups about scale, cross-team coordination, and operational load.
- →Anchor on the SLA and data shape before diagramming
- →Discuss idempotency, partitioning, and backfill explicitly
- →Estimate cost: 'This pipeline will cost roughly $X/month at this volume'
05Onsite: technical + culture
60 minRigorous technical deep-dive blended with Goldman's values interview. Expect high expectations for both.
- →Goldman's 14 Business Principles are actually referenced
- →Intellectual rigor and depth of reasoning are central
- →Don't pretend to know things you don't; Goldman interviewers catch it
Level bar
What Goldman Sachs expects at L5 Senior Data Engineer
Independent technical leadership
Senior DEs drive pipeline designs without engineering manager involvement. Interviewers probe whether you can decompose ambiguous requirements, make architecture trade-offs, and defend your choices under scrutiny.
Cross-team coordination
Senior scope regularly spans multiple teams. Expect scenarios about a downstream team missing an SLA because of a change you made, or negotiating a schema migration with the team that owns the upstream source.
Production operational rigor
Fluent in on-call, alerting, data quality checks, and incident response. Dive-deep stories at this level should include correlating a metric drop to a specific commit or a timezone bug or a subtle ordering issue, not 'I looked at the logs.'
Goldman Sachs-specific emphasis
Goldman Sachs's loop is characterized by: Investment-bank rigor with Marcus/transaction-banking modernization and strats/quant culture. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.
Behavioral
How Goldman Sachs frames behavioral rounds
Integrity
Banking integrity is existential. Goldman interviewers probe seriously.
Excellence
Goldman's brand depends on it. Sloppy work stands out negatively.
Client focus
Even for engineers, Goldman is client-first. Internal-only product mindset doesn't fit.
Partnership
Goldman's structure emphasizes cross-divisional collaboration. Solo operators fail.
Prep timeline
Week-by-week preparation plan
Foundations and gap analysis
- ·Do 10 medium SQL problems. Note which patterns feel slow
- ·Write out 2-3 behavioral stories per value, Goldman Sachs weights this round heavily
- ·Read Goldman Sachs's public engineering blog for recent architecture patterns
- ·Review your prior production work, pick 3-5 projects you can discuss in depth
SQL and coding fluency
- ·Practice window functions until DENSE_RANK, ROW_NUMBER, LAG, LEAD are reflex
- ·Do 20+ Goldman Sachs-style problems in their domain
- ·Time yourself: 25 min per medium, 35 min per hard
- ·Record yourself narrating approach aloud, communication is graded
Pipeline system design
- ·Design 5 pipelines on paper: daily aggregation, clickstream, CDC, ML feature store, real-time alerting
- ·For each, write SLA, partition strategy, backfill plan, and cost estimate
- ·Practice with a friend, senior-level system design is 50% driving the conversation
- ·Review Goldman Sachs's open-source and engineering blog for in-house patterns
Behavioral polish and mock loops
- ·Rehearse every story out loud. Cut to 2-3 minutes each
- ·Run 2 full mock loops with a senior DE or coach
- ·Identify your 3 weakest behavioral areas and draft additional stories
- ·Review recent Goldman Sachs news or earnings call for fresh talking points
Taper and logistics
- ·No new content. Review your notes only
- ·Sleep. Mental energy matters more than one more practice problem
- ·Confirm logistics: laptop charged, shared-doc tool tested, snack and water nearby
- ·Remember: the loop is rooting for you to raise the bar, not to fail
See also
Related pages on Goldman Sachs's loop
FAQ
Common questions
- What level is Senior Data Engineer at Goldman Sachs?
- On Goldman Sachs's ladder, Senior Data Engineer sits at L5. Expectations center on independent technical leadership and cross-team influence.
- How much does a Goldman Sachs Senior Data Engineer make?
- Total compensation for Goldman Sachs Senior Data Engineer ranges $190K–$240K base • $350K–$500K total. Ranges shift by team and negotiation.
- How is the Senior Data Engineer loop different from other levels at Goldman Sachs?
- Round structure is shared across levels; what changes is what each round tests. For Senior Data Engineer the emphasis is independent technical leadership and cross-team influence, with particular attention to independent system design and cross-team influence.
- How long should I prepare for the Goldman Sachs Senior Data Engineer interview?
- 8-10 weeks of focused prep is typical for candidates already working as a DE. Less than 4 weeks is tight; the behavioral story bank usually takes longer than candidates expect.
- Does Goldman Sachs interview data engineers differently than software engineers?
- Yes. DE loops at Goldman Sachs weight SQL heavier, include pipeline/system-design rounds tuned to data workloads, and probe for production data experience (ingestion patterns, data quality, backfill) that generalist SWE loops skip.