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Media, Streaming & Telecom

Disney+ Hotstar

OTT streaming at massive concurrency · Data Engineering interview

Strong DSA + DBMS bar (Hotstar SDE roots) plus DE-specific SQL, Spark optimization, and peak-scale streaming design.

Cloud / platform

AWS; Spark, Kafka, Hive/Presto, Flink

Reported difficulty

Not reported

SQLPythonSparkHiveKafka

Hiring process

OA (coding + MCQs)

DSA ×2

Hiring manager (system design)

Offer

Round-by-round breakdown

Online Assessment

High confidence

60 min: 2 coding + 20 MCQs (DS, OS, DBMS)

DSA / Coding

High confidence

Reverse a linked list (recursive + iterative; in parts)
Intersection and union of two arrays

SQL (DE)

Medium confidence

Window functions; sessionize playback events; top-N

Spark / DBMS (HM)

Medium confidence

Spark optimizations, partitioning, joins, large datasets
SQL vs NoSQL, indexing

System & pipeline design prompts

High-concurrency playback/clickstream analytics pipeline (IPL-scale peaks)
Batch vs streaming, lakehouse patterns (senior DE)

Prep — high priority

DSA (linked lists, arrays) + SQL window functions
Spark optimization (skew/partition/joins/OOM)
Peak-scale streaming design

Prep — medium priority

Data modeling/warehouse; DBMS (SQL vs NoSQL, indexing)
Behavioral scale stories

Rejection patterns

Weak DSA at OA/coding rounds (Hotstar bar is SDE-like)
Shallow Spark optimization
Cannot design for peak concurrency

Reported experiences

SDE-1 (Hotstar)

ReportedFirst-hand

OA (2 coding + 20 MCQs) → 2 DSA rounds (linked-list reversal incl. in parts; array intersection/union) → HM system design + DBMS (SQL vs NoSQL, indexing) → HR. The coding + DBMS bar applies to DE.

Rehearse for Disney+ Hotstar

Answered questions on what Disney+ Hotstar 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…

Media, Streaming & Telecom

Disney+ Hotstar

OTT streaming at massive concurrency · Data Engineering interview

Strong DSA + DBMS bar (Hotstar SDE roots) plus DE-specific SQL, Spark optimization, and peak-scale streaming design.

Cloud / platform

AWS; Spark, Kafka, Hive/Presto, Flink

Reported difficulty

Not reported

SQLPythonSparkHiveKafka

Hiring process

OA (coding + MCQs)

DSA ×2

Hiring manager (system design)

Offer

Round-by-round breakdown

Online Assessment

High confidence

60 min: 2 coding + 20 MCQs (DS, OS, DBMS)

DSA / Coding

High confidence

Reverse a linked list (recursive + iterative; in parts)
Intersection and union of two arrays

SQL (DE)

Medium confidence

Window functions; sessionize playback events; top-N

Spark / DBMS (HM)

Medium confidence

Spark optimizations, partitioning, joins, large datasets
SQL vs NoSQL, indexing

System & pipeline design prompts

High-concurrency playback/clickstream analytics pipeline (IPL-scale peaks)
Batch vs streaming, lakehouse patterns (senior DE)

Prep — high priority

DSA (linked lists, arrays) + SQL window functions
Spark optimization (skew/partition/joins/OOM)
Peak-scale streaming design

Prep — medium priority

Data modeling/warehouse; DBMS (SQL vs NoSQL, indexing)
Behavioral scale stories

Rejection patterns

Weak DSA at OA/coding rounds (Hotstar bar is SDE-like)
Shallow Spark optimization
Cannot design for peak concurrency

Reported experiences

SDE-1 (Hotstar)

ReportedFirst-hand

Rehearse for Disney+ Hotstar

Answered questions on what Disney+ Hotstar 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