What level is Data Engineer at Goldman Sachs?

At Goldman Sachs, Data Engineer corresponds to the L4 level. The bar emphasizes shipped production pipelines end-to-end and can debug them when they break without people-management responsibilities.

How much does a Goldman Sachs Data Engineer make?

Looking at 102 sampled offers from 2021-2026, Goldman Sachs Data Engineer total comp comes in at $145K median, ranging from $94K to $237K, median base $132K and median annual equity $16K. Typical experience range: 4-12 years..

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

Most working DEs find 6-8 weeks is about right. The technical prep scales with experience; the behavioral story bank is where candidates underestimate time.

Does Goldman Sachs interview data engineers differently than software engineers?

Yes, the DE track at Goldman Sachs emphasizes SQL depth, warehouse and pipeline design, and real production data experience (late data, backfills, quality checks), which generalist SWE loops don't test.

Interview Guide · 2026

Goldman Sachs Data Engineer Interview (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.

Compensation

$155K–$190K base • $240K–$350K total

Loop duration

3 hours onsite

Rounds

4 rounds

Location

New York, Dallas, Salt Lake City, London, Bangalore

Compensation

Goldman Sachs Data Engineer total comp

Across 102 samples

Offer-report aggregate, 2021-2026. Level mapped: L4. Typical experience: 4-12 years (median 7).

25th percentile

$94K

Median total comp

$145K

75th percentile

$237K

Median base salary

$132K

Median annual equity

$16K

Median total comp by year

2022

$254K n=4

2023

$238K n=5

2024

$232K n=14

2025

$120K n=26

2026

$127K n=51

Tech stack

What Goldman Sachs data engineers actually use

Across 6 open roles

These are the tools that show up in Goldman Sachs's DE job descriptions right now. Click any chip to drop into an interview prep page for it.

Java5SQL4 Python4 Kafka3 Spark3 Hadoop2AWS2Snowflake2 Scala1 Airflow1Terraform1Azure1CI/CD1Databricks1 Flink1

Round focus

Domain concentration by round

Across 6 job descriptions

Where each domain tends to come up in Goldman Sachs's loop, derived from 6 current data engineer job descriptions. Longer bars mean heavier weight.

Online Assessment

Python85%

SQL44%

Architecture22%

Modeling5%

Spark3%

Phone Screen

Python61%

SQL60%

Architecture33%

Modeling12%

Onsite Loop

Architecture61%

Modeling41%

Python36%

SQL32%

Spark3%

Practice problems

Goldman Sachs data engineer practice set

4 problems

Interview problems predicted for Goldman Sachs data engineers based on their actual job descriptions. Click any problem to work it in a live coding environment.

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~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→

Architecturemedium~25 min

The Queue That Wouldn't Stop Growing

Your streaming video event pipeline shows consumer lag spiking from near-zero to over 500,000 messages within two hours. You need to diagnose whether the cause is a producer burst or a consumer slowdown, then design a monitoring and auto-remediation system that can detect, alert on, and automatically recover from future lag events.

Open in practice environment→

Pythoneasy~10 min

The Spread

Given a list of numbers, return the sample variance (sum of squared deviations divided by n-1), rounded to 2 decimals. Return 0.0 when fewer than 2 numbers.

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.

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

Adjacent guides to check

Goldman Sachs Junior Data Engineer→Goldman Sachs Senior Data Engineer→Goldman Sachs Staff Data Engineer→Goldman Sachs Principal Data Engineer→Amazon Data Engineer→Meta Data Engineer→Google Data Engineer→Goldman Sachs Data Engineer Salary→Data Engineer Interview Prep (hub)→

FAQ

Common questions

What level is Data Engineer at Goldman Sachs?: At Goldman Sachs, Data Engineer corresponds to the L4 level. The bar emphasizes shipped production pipelines end-to-end and can debug them when they break without people-management responsibilities.
How much does a Goldman Sachs Data Engineer make?: Looking at 102 sampled offers from 2021-2026, Goldman Sachs Data Engineer total comp comes in at $145K median, ranging from $94K to $237K, median base $132K and median annual equity $16K. Typical experience range: 4-12 years..
How is the Data Engineer loop different from other levels at Goldman Sachs?: The format of the loop matches other levels; difficulty and evaluation shift to shipped production pipelines end-to-end and can debug them when they break, and questions at this level dig into production pipeline ownership and on-call debugging.
How long should I prepare for the Goldman Sachs Data Engineer interview?: Most working DEs find 6-8 weeks is about right. The technical prep scales with experience; the behavioral story bank is where candidates underestimate time.
Does Goldman Sachs interview data engineers differently than software engineers?: Yes, the DE track at Goldman Sachs emphasizes SQL depth, warehouse and pipeline design, and real production data experience (late data, backfills, quality checks), which generalist SWE loops don't test.

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