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 37 offer samples from 2021-2026, Goldman Sachs Data Engineer in New York total compensation lands at $136K (P25), $250K (median), and $306K (P75), median base $192K 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 · 2026

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 37 samples

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

25th percentile

$136K

Median total comp

$250K

75th percentile

$306K

Median base salary

$192K

Median annual equity

$18K

Median total comp by year

2024

$362K n=6

2025

$150K n=8

2026

$228K n=18

1 currently open data engineer postings in New York.

Tech stack

What Goldman Sachs data engineers actually use

Across 1 open roles

Frequency of each tool across Goldman Sachs's open DE postings in New York. The ones with interview prep pages are live links.

Airflow1AWS1Flink1 Hadoop1Java1Python1 Scala1 Snowflake1 Spark1 SQL1

Round focus

Domain concentration by round

Across 1 job descriptions

Goldman Sachs's round-by-round focus, inferred from 1 active data engineer job descriptions. Use this to calibrate which domains to drill for each round.

Online Assessment

Python84%

SQL46%

Architecture24%

Modeling6%

Spark3%

Phone Screen

SQL60%

Python59%

Architecture32%

Modeling12%

Onsite Loop

Architecture59%

Modeling46%

Python39%

SQL34%

Spark4%

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.

SQLeasy~5 min

Clean Latency Cast

During a data quality investigation, you found that the latency column in service health records contains some non-numeric strings. Return all records with latency converted to an integer, excluding any rows where the conversion would fail. Return all available fields.

Open in practice environment→

Pythonmedium~10 min

The Calendar Untangled

Given a list of 'YYYY-MM-DD' date strings, return them sorted chronologically ascending. Lexicographic comparison happens to be correct for this format.

Open in practice environment→

Architecturehard~25 min

Pharma Data Ingestion Pipeline with Governance

We're a pharmaceutical company ingesting data from clinical trial systems, commercial sales databases, and patient support program feeds. The data governance team has mandated that every dataset entering the warehouse must have a documented data quality check, a lineage trace, and an access control policy before it goes live. Design the ingestion pipeline and governance framework.

Open in practice environment→

Pythonmedium~20 min

The Coin Vault

Given a target amount and a list of coin denominations, return the minimum coins needed using a greedy strategy: repeatedly take the largest coin that does not exceed the remaining amount. Return -1 if the greedy approach cannot make exact change.

Open in practice environment→

Try itTop 2 sellers by revenue in each marketplace

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

top_sellers.sql

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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;
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;

Click Run to execute. Edit the code above to experiment.

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.

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 test 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 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 approach aloud, communication is graded

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)→Goldman Sachs Junior Data Engineer in New York→Goldman Sachs Senior Data Engineer in New York→Goldman Sachs Staff Data Engineer in New York→Goldman Sachs Principal Data Engineer in New York→Goldman Sachs Data Engineer in London→Goldman Sachs Data Engineer Salary→Data Engineer Interview Prep (hub)→

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 37 offer samples from 2021-2026, Goldman Sachs Data Engineer in New York total compensation lands at $136K (P25), $250K (median), and $306K (P75), median base $192K 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.

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Published 2026-04-20Updated 2026-04-20