What level is Junior Data Engineer at Goldman Sachs?

Goldman Sachs uses L3 to designate Junior Data Engineers; this is an IC-track level focused on foundational SQL fluency and a willingness to learn production systems.

How much does a Goldman Sachs Junior Data Engineer make?

Goldman Sachs Junior Data Engineer offers span $79K-$118K across 7 samples from 2021-2026, with a median of $100K, median base $90K. Typical experience range: 7-8 years..

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

6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.

Does Goldman Sachs interview data engineers differently than software engineers?

The tracks diverge. DE at Goldman Sachs weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.

Interview Guide · 2026

Goldman Sachs Junior Data Engineer Interview (L3)

Goldman Sachs's Junior Data Engineer loop ((L3) short) emphasizes Investment-bank rigor with Marcus/transaction-banking modernization and strats/quant culture. Candidates who clear it demonstrate foundational SQL fluency and a willingness to learn production systems backed by roughly 0-2 years.

Compensation

$125K–$155K base • $170K–$235K total

Loop duration

3 hours onsite

Rounds

4 rounds

Location

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

Compensation

Goldman Sachs Junior Data Engineer total comp

Across 7 samples

Offer-report aggregate, 2021-2026. Level mapped: L3. Typical experience: 7-8 years (median 7).

25th percentile

$79K

Median total comp

$100K

75th percentile

$118K

Median base salary

$90K

Tech stack

What Goldman Sachs junior data engineers actually use

Across 6 open roles

Tools and languages mentioned most often in Goldman Sachs's currently-active data engineer postings. Each chip links to an interview prep page for that tool.

Java5SQL4 Python4 Kafka3 Spark3 Hadoop2AWS2Snowflake2 Scala1 Airflow1Terraform1Azure1CI/CD1Databricks1 Flink1

Round focus

Domain concentration by round

Across 6 job descriptions

What each Goldman Sachs round typically tests, weighted across 6 live junior data engineer postings. The bars show the relative emphasis of each domain.

Online Assessment

Python85%

SQL44%

Architecture22%

Modeling5%

Spark3%

Phone Screen

Python61%

SQL60%

Architecture33%

Modeling12%

Onsite Loop

Architecture61%

Modeling41%

Python36%

SQL32%

Spark3%

Practice problems

Goldman Sachs junior data engineer practice set

4 problems

Practice sets surfaced for Goldman Sachs junior data engineer candidates by the same model that reads their job postings. Each card opens a working coding environment.

SQLmedium~10 min

Binary Flag Indicators

The feature flag dashboard needs a clean boolean representation for downstream consumers. For each flag, show the flag name, a 1/0 indicator for whether it is enabled, and a 1/0 indicator for whether it is disabled.

Open in practice environment→

Pythonmedium~10 min

Detect Cycle in Sequence

You are given a list of integers where each value at index i is the next index to visit (or -1 to terminate). Starting from index 0, follow the chain and return True if you revisit any index, False otherwise. Out-of-range indices (including -1) count as termination, not a cycle.

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→

SQLeasy~5 min

Top Performing Models

The ML registry tracks model accuracy. Surface all models with accuracy at 0.90 or above. Return all available fields for each qualifying model, sorted from highest accuracy to lowest.

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 Junior Data Engineer

SQL foundations

Junior rounds weight SQL the heaviest. Expect multi-table joins, aggregations, window functions, and one harder query involving self-joins or recursive CTEs. You do not need to design systems at this level, but you do need SQL to be reflexive.

Learning orientation

Interviewers probe how you pick up new tools. A strong story about learning a new stack in a prior role (even an internship or side project) can outweigh gaps in production experience.

Basic pipeline awareness

You should know what ETL vs ELT means, what a data warehouse is, and why idempotency matters, even if you have not built a production pipeline yourself.

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 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
·Shore up data engineering foundations: SQL, Python, one warehouse (Snowflake/BigQuery/Redshift)

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

Other guides you'll want

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

FAQ

Common questions

What level is Junior Data Engineer at Goldman Sachs?: Goldman Sachs uses L3 to designate Junior Data Engineers; this is an IC-track level focused on foundational SQL fluency and a willingness to learn production systems.
How much does a Goldman Sachs Junior Data Engineer make?: Goldman Sachs Junior Data Engineer offers span $79K-$118K across 7 samples from 2021-2026, with a median of $100K, median base $90K. Typical experience range: 7-8 years..
How is the Junior Data Engineer loop different from other levels at Goldman Sachs?: Junior Data Engineer loops run the same stages as other levels, but interviewers calibrate difficulty to foundational SQL fluency and a willingness to learn production systems, especially around SQL fundamentals, learning orientation, and basic pipeline awareness.
How long should I prepare for the Goldman Sachs Junior Data Engineer interview?: 6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.
Does Goldman Sachs interview data engineers differently than software engineers?: The tracks diverge. DE at Goldman Sachs weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.

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