Swiggy

Three same-day rounds: Python/DSA/SQL (CTE + window), then DE depth/PySpark and a problem-solving/managerial close.

Cloud / platform

AWS; Spark, Kafka, Databricks/warehouse

Reported difficulty

Not reported

PythonPySparkSQLDSAPandas

Hiring process

R1 Python/DSA/SQL

R2 DE deep-dive / ML

R3 problem-solving + managerial

Offer

Round-by-round breakdown

3

01

SQL

High confidence

Find the 2 most paid employees from each department (CTE + window functions)
Subqueries; top-N per group; order/delivery analytics

02

Python / PySpark

Medium confidence

Functions, try-except, loops, decorators, multithreading
Basic Pandas (groupby); PySpark

03

Problem-solving / Managerial

Medium confidence

Real-world team problems (e.g., delivery-time estimation, dynamic filters)

System & pipeline design prompts

1

Order/delivery pipeline: ingestion → processing → serving → monitoring

Rejection patterns

3

Weak SQL (CTE/window/top-N)
Shaky Python (decorators/multithreading)
Thin problem-solving in R3

Reported experiences

1

Data Scientist 1 (closest first-hand)

ReportedFirst-hand

Python/DSA/SQL round (CTE+window: 2 highest-paid per dept; subqueries; decorators/multithreading) → ML round → problem-solving/managerial. The R1 SQL+Python bar applies to DE.

Hiring process

Round-by-round breakdown

SQL

Python / PySpark

Problem-solving / Managerial

System & pipeline design prompts

Prep — high priority

Prep — medium priority

Rejection patterns

Reported experiences

Data Scientist 1 (closest first-hand)

Rehearse for Swiggy

Swiggy

Hiring process

Round-by-round breakdown

SQL

Python / PySpark

Problem-solving / Managerial

System & pipeline design prompts

Prep — high priority

Prep — medium priority

Rejection patterns

Reported experiences

Data Scientist 1 (closest first-hand)

Rehearse for Swiggy