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Finance, Payments & Fintech

Goldman Sachs

Investment banking / financial services · Data Engineering interview

CoderPad SQL + Python, distributed-systems fundamentals (RDD vs DF, why PySpark), data modeling, and a hiring committee.

Cloud / platform

Hybrid; big Spark/Hadoop; internal platforms

Reported difficulty

Not reported

SQLPythonSparkHDFSData modeling

Hiring process

Recruiter

CoderPad

Virtual technical (1–2)

Onsite (3–5)

Hiring committee

Offer

Round-by-round breakdown

SQL

· medium–hardHigh confidence

Multi-join queries, window functions, CTEs
Performance optimization
Top-N, running totals, gaps-and-islands

Python / CoderPad

Medium confidence

Data manipulation + algorithms
Arrays, trees, recursion, simple graph traversal

Distributed systems / Spark

Medium confidence

Spark, HDFS, YARN; why PySpark exists
JVMs; Java vs Scala
RDDs vs DataFrames; Spark optimization scenarios

System & pipeline design prompts

Markets/risk data pipeline (ingestion → Spark → serving): correctness, lineage, governance

Prep — high priority

SQL medium–hard (multi-join, window, CTE, optimization)
Python CoderPad (arrays/trees/recursion/graphs)
Spark/distributed fundamentals (RDD vs DF, HDFS/YARN, why PySpark, Java vs Scala)

Prep — medium priority

Data modeling + ETL architecture
Behavioral/values

Rejection patterns

Weak SQL under medium–hard load
Shaky Spark/distributed fundamentals (RDD vs DF, why PySpark)
Shallow data modeling

Reported experiences

Data Engineering Senior Analyst

ReportedFirst-hand

Recruiter → CoderPad/technical (SQL + Python) → distributed systems (Spark/HDFS/YARN, RDD vs DF) → data modeling/architecture → behavioral.

Rehearse for Goldman Sachs

Answered questions on what Goldman Sachs actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences

Openingloading the content…

Finance, Payments & Fintech

Goldman Sachs

Investment banking / financial services · Data Engineering interview

CoderPad SQL + Python, distributed-systems fundamentals (RDD vs DF, why PySpark), data modeling, and a hiring committee.

Cloud / platform

Hybrid; big Spark/Hadoop; internal platforms

Reported difficulty

Not reported

SQLPythonSparkHDFSData modeling

Hiring process

Recruiter

CoderPad

Virtual technical (1–2)

Onsite (3–5)

Hiring committee

Offer

Round-by-round breakdown

SQL

· medium–hardHigh confidence

Multi-join queries, window functions, CTEs
Performance optimization
Top-N, running totals, gaps-and-islands

Python / CoderPad

Medium confidence

Data manipulation + algorithms
Arrays, trees, recursion, simple graph traversal

Distributed systems / Spark

Medium confidence

Spark, HDFS, YARN; why PySpark exists
JVMs; Java vs Scala
RDDs vs DataFrames; Spark optimization scenarios

System & pipeline design prompts

Markets/risk data pipeline (ingestion → Spark → serving): correctness, lineage, governance

Prep — high priority

SQL medium–hard (multi-join, window, CTE, optimization)
Python CoderPad (arrays/trees/recursion/graphs)
Spark/distributed fundamentals (RDD vs DF, HDFS/YARN, why PySpark, Java vs Scala)

Prep — medium priority

Data modeling + ETL architecture
Behavioral/values

Rejection patterns

Weak SQL under medium–hard load
Shaky Spark/distributed fundamentals (RDD vs DF, why PySpark)
Shallow data modeling

Reported experiences

Data Engineering Senior Analyst

ReportedFirst-hand

Recruiter → CoderPad/technical (SQL + Python) → distributed systems (Spark/HDFS/YARN, RDD vs DF) → data modeling/architecture → behavioral.

Rehearse for Goldman Sachs

Answered questions on what Goldman Sachs actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences