SAP

Whiteboard coding + SQL/processing + conceptual rounds; SQL optimization, Spark (RDD vs DF, Catalyst), and pipeline design.

Cloud / platform

SAP BTP, HANA; + AWS/Azure/GCP; Spark

Reported difficulty

Not reported

SQLPythonSparkETLData warehousing

Hiring process

Background/projects

Live Python (Coderpad)

SQL + processing

Conceptual (ML/DE)

Behavioral

Offer

Round-by-round breakdown

3

01

SQL

Medium confidence

Window functions, CTEs, subqueries, GROUP BY pitfalls, indexing, optimization
Find the top 3 most active users per day

02

Python

Medium confidence

Live Coderpad; data manipulation

03

Spark

Medium confidence

RDD vs DataFrame
Query optimization with Catalyst
Performance tuning (skew, shuffle)

System & pipeline design prompts

1

Real-time pipeline / lakehouse / CDC / batch ETL at scale (Lambda vs Kappa, partitioning, idempotency, fault tolerance)

Rejection patterns

3

Weak SQL optimization/window
Shaky Spark internals
Shallow pipeline design (no idempotency/fault tolerance)

Reported experiences

1

Data Engineer

IllustrativeComposite

Composite from SAP (Data Scientist) + general DE signals: background → live Python → SQL (top-3 active users/day) → Spark (RDD vs DF, Catalyst) → pipeline design → behavioral.

Hiring process

Round-by-round breakdown

SQL

Python

Spark

System & pipeline design prompts

Prep — high priority

Prep — medium priority

Rejection patterns

Reported experiences

Data Engineer

Rehearse for SAP

SAP

Hiring process

Round-by-round breakdown

SQL

Python

Spark

System & pipeline design prompts

Prep — high priority

Prep — medium priority

Rejection patterns

Reported experiences

Data Engineer

Rehearse for SAP