Mastercard

HackerRank/live coding then a "Super Day": SQL window functions, Spark repartition-vs-coalesce, and Python data munging.

Cloud / platform

Hybrid; Hadoop/Spark/Hive, Redshift; Kafka

Reported difficulty

Not reported

SQLPythonSparkRedshiftHive

Hiring process

Recruiter

Assessment (SQL + Python/Java)

Super Day (3–4)

System design

Behavioral

Offer

Round-by-round breakdown

3

01

SQL

High confidence

Find the second highest salary from an employee table
Write queries with ROW_NUMBER() and RANK(); explain window functions
Top-N per group, dedup, aggregations

02

Python

Medium confidence

Write a Python function to flatten nested lists
Handle exceptions in data-pipeline scripts

03

Spark

High confidence

repartition() vs coalesce() with use cases
How joins work in Spark; avoiding shuffle-heavy operations

System & pipeline design prompts

1

Payments/transaction pipeline: dedup/idempotency, correctness, governance

Rejection patterns

3

Weak window functions
Cannot explain repartition vs coalesce / shuffle avoidance
Sloppy Python error handling

Reported experiences

1

Senior Data Engineer

ReportedFirst-hand

LinkedIn account (Naman Seth) confirming recruiter → assessment → Super Day with deep technical + 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

Senior Data Engineer

Rehearse for Mastercard

Mastercard

Hiring process

Round-by-round breakdown

SQL

Python

Spark

System & pipeline design prompts

Prep — high priority

Prep — medium priority

Rejection patterns

Reported experiences

Senior Data Engineer

Rehearse for Mastercard