What level is Data Engineer at Goldman Sachs?

On Goldman Sachs's ladder, Data Engineer sits at L4. Expectations center on shipped production pipelines end-to-end and can debug them when they break.

How much does a Goldman Sachs Data Engineer in New York make?

Across 38 offer samples from 2021-2026, Goldman Sachs Data Engineer in New York total compensation lands at $137K (P25), $249K (median), and $302K (P75), median base $191K and median annual equity $18K. Typical experience range: 3-16 years..

Does Goldman Sachs actually hire data engineers in New York?

Yes, Goldman Sachs maintains a New York office and hires Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.

How is the Data Engineer loop different from other levels at Goldman Sachs?

Round structure is shared across levels; what changes is what each round tests. For Data Engineer the emphasis is shipped production pipelines end-to-end and can debug them when they break, with particular attention to production pipeline ownership and on-call debugging.

How long should I prepare for the Goldman Sachs Data Engineer interview?

6-8 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.

Interview Guide

Goldman Sachs Data Engineer Interview in New York (L4)

Goldman Sachs (L4) Data Engineer loop: Investment-bank rigor with Marcus/transaction-banking modernization and strats/quant culture. Bar at this level: shipped production pipelines end-to-end and can debug them when they break. Typical 2-5 years of data engineering experience. Below we dig into how this runs out of the New York office (New York, NY), with cost-of-living-adjusted compensation.

Compensation

$155K–$190K base • $240K–$350K total

Loop duration

3 hours onsite

Rounds

4 rounds

Location

New York, NY

Compensation

Goldman Sachs Data Engineer in New York total comp

Across 38 samples

Offer-report aggregate, 2021-2026. Level mapped: L4. Typical experience: 3-16 years (median 8).

25th percentile

$137K

Median total comp

$249K

75th percentile

$302K

Median base salary

$191K

Median annual equity

$18K

Median total comp by year

2024

$362K n=6

2025

$150K n=8

2026

$206K n=19

Practice problems

Goldman Sachs data engineer practice set

4 problems

Problems the Goldman Sachs data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.

SQLmedium~5 min

Active Duo

The growth team is building a cross-engagement segment of users who both make purchases and log browsing sessions on the platform. Return a deduplicated list of usernames for users with activity in both areas.

Open in practice environment→

Pythonmedium~5 min

Detect Outliers

Given a list of numbers and multiplier k, compute the mean. Return the sorted list of indices where the value exceeds k * mean.

Open in practice environment→

SQLhard~22 min

Users Who Churned in February

Find all users who had sessions in January 2026 but none in February 2026.

Open in practice environment→

Pythoneasy~10 min

Quantile Calculator

Given a list of numbers and percentile (0-100), return the value at that percentile using linear interpolation. The index is percentile / 100 * (n - 1); if fractional, linearly interpolate between the floor and ceiling indices of the sorted values.

Open in practice environment→

Prepare for the interview

01 / Open invite

02min.

Walk into Goldman Sachs knowing the system design pattern they'll test.

a Goldman Sachs system design query, the same shape a screen would give you.

The diff against expected. Where ties broke. What you missed.

sandbox

1source → bronze → silver → gold

2 ingest : CDC + Kafka

3 transform : dbt + Airflow

4 serve : Snowflake

Execute your solution0.4s avg.

Goldman SachsInterview question

Solve a Goldman Sachs problem

Top 2 sellers by revenue in each marketplace

Classic DE round opener. Window function + partition. Edit to tweak the threshold.

	WITH seller_totals AS (
	SELECT
	marketplace,
	seller_id,
	SUM(amount) AS revenue
	FROM seller_orders
	GROUP BY marketplace, seller_id
	),
	ranked AS (
	SELECT
	marketplace,
	seller_id,
	revenue,
	DENSE_RANK() OVER (
	PARTITION BY marketplace
	ORDER BY revenue DESC
	) AS rk
	FROM seller_totals
	)

	SELECT
	marketplace,
	seller_id,
	revenue
	FROM ranked
	WHERE rk <= 2
	ORDER BY marketplace, revenue DESC

New York, NY

Goldman Sachs in New York

Finance-adjacent DE work is common; fintech and trading firms compete with Big Tech on comp. Required comp range disclosures in NY job postings.

Goldman Sachs's New York office hires at the company's reference compensation band. The interview loop itself is identical to Goldman Sachs's global process in New York; local variation shows up in team and compensation.

Prepare for the interview

03 / From the bank03 of many

03hand-picked.

Two Years of Every Click

Hard30 min

Every click, every aisle, every day for two years.

Open the sandbox

Pick your level

Goldman SachsL3Disappearing Ink TeslaL4Two Hundred Million Redirects NvidiaL5The Decision Before the Door Closes

Pulled from debriefs where system design separated levels.

The loop

How the interview actually runs

01Recruiter screen

30 min

Goldman 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 min

SQL + 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 min

Design 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

04Onsite: technical + culture

60 min

Rigorous 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 L4 Data Engineer

Pipeline ownership

Mid-level DEs own pipelines end-to-end. Interviewers expect stories about designing, deploying, and maintaining a data pipeline that has been in production for 6+ months.

SQL + Python or Spark fluency

SQL is the floor. Most teams also expect fluency in either Python for data manipulation (pandas, airflow DAGs) or Spark for larger-scale processing.

On-call debugging

You should have concrete stories about production incidents: what alert fired, how you diagnosed, what you fixed, and what post-mortem action you owned.

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.

“Describe a situation where you chose the harder right over the easier wrong.”

Excellence

Goldman's brand depends on it. Sloppy work stands out negatively.

“What work of yours would you point to as your best?”

Client focus

Even for engineers, Goldman is client-first. Internal-only product mindset doesn't fit.

“Tell me about a time you prioritized a client need over your team's preferences.”

Partnership

Goldman's structure emphasizes cross-divisional collaboration. Solo operators fail.

“Describe collaborating with a team whose incentives differed from yours.”

Prep timeline

Week-by-week preparation plan

8-10 weeks out

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

6 weeks out

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 your approach aloud. Interviewers weigh how you explain it, not only what you write

4 weeks out

Pipeline awareness and behavioral depth

·Review pipeline architecture basics: idempotency, partitioning, backfill
·Practice explaining a pipeline you've worked on end-to-end in 5 minutes
·Refine behavioral stories based on mock feedback
·Do 10 more SQL problems at medium difficulty

2 weeks out

Behavioral polish and mock loops

·Rehearse every story out loud. Cut to 2-3 minutes each
·Run 2 full mock loops with a mid-level 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

Week of

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: interviewers want to find reasons to hire you, not to reject you

Related pages on Goldman Sachs's loop

Goldman Sachs Data Engineer (global guide)→Data Engineer Interview Prep→

FAQ

Common questions

What level is Data Engineer at Goldman Sachs?: On Goldman Sachs's ladder, Data Engineer sits at L4. Expectations center on shipped production pipelines end-to-end and can debug them when they break.
How much does a Goldman Sachs Data Engineer in New York make?: Across 38 offer samples from 2021-2026, Goldman Sachs Data Engineer in New York total compensation lands at $137K (P25), $249K (median), and $302K (P75), median base $191K and median annual equity $18K. Typical experience range: 3-16 years..
Does Goldman Sachs actually hire data engineers in New York?: Yes, Goldman Sachs maintains a New York office and hires Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
How is the Data Engineer loop different from other levels at Goldman Sachs?: Round structure is shared across levels; what changes is what each round tests. For Data Engineer the emphasis is shipped production pipelines end-to-end and can debug them when they break, with particular attention to production pipeline ownership and on-call debugging.
How long should I prepare for the Goldman Sachs Data Engineer interview?: 6-8 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.