Openingloading the content…

Loadingone moment…

Data & Cloud Platforms

Datadog

Observability / monitoring SaaS · Data Engineering interview

Hybrid practical/LeetCode coding (Python or Go), a production-quality take-home, and DB-type trade-off system design.

Cloud / platform

AWS-heavy; Kafka, Spark, time-series infra

Reported difficulty

Not reported

PythonGoSQLNoSQLTime-series

Hiring process

Recruiter (30m)

Coding (2 engineers)

Take-home project

Design interview

Offer

Round-by-round breakdown

Coding

· Coderpad, 2 engineersMedium confidence

2 "fairly simple" exercises
Internal bank: hybrid practical + LeetCode (start simple, then add complexity)
Python or Go

Take-home project

· production-qualityMedium confidence

Code + documentation + unit tests + formatting

Databases / Design

Medium confidence

Strengths/weaknesses of DB types — use cases, performance, scalability
Design an architecture for a specific application

System & pipeline design prompts

Architecture for a given application; SQL vs NoSQL vs time-series trade-offs
High-volume metrics/log ingestion & query pipeline

Prep — high priority

Clean coding (Python/Go) incl. take-home quality (unit tests, docs, formatting)
DB-type trade-offs (SQL/NoSQL/time-series)
App architecture design

Prep — medium priority

SQL over metrics/log data
Time-series storage
Behavioral

Rejection patterns

Weak take-home (no tests/docs/formatting)
Cannot reason about DB-type trade-offs
Shallow coding under added complexity

Reported experiences

Data Engineer

ReportedFirst-hand

Recruiter (behavioral) → 2-engineer coding (Coderpad, 2 simple exercises) → take-home (code, docs, unit tests, formatting) → design (architecture for an app).

Rehearse for Datadog

Answered questions on what Datadog 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…

Data & Cloud Platforms

Datadog

Observability / monitoring SaaS · Data Engineering interview

Hybrid practical/LeetCode coding (Python or Go), a production-quality take-home, and DB-type trade-off system design.

Cloud / platform

AWS-heavy; Kafka, Spark, time-series infra

Reported difficulty

Not reported

PythonGoSQLNoSQLTime-series

Hiring process

Recruiter (30m)

Coding (2 engineers)

Take-home project

Design interview

Offer

Round-by-round breakdown

Coding

· Coderpad, 2 engineersMedium confidence

2 "fairly simple" exercises
Internal bank: hybrid practical + LeetCode (start simple, then add complexity)
Python or Go

Take-home project

· production-qualityMedium confidence

Code + documentation + unit tests + formatting

Databases / Design

Medium confidence

Strengths/weaknesses of DB types — use cases, performance, scalability
Design an architecture for a specific application

System & pipeline design prompts

Architecture for a given application; SQL vs NoSQL vs time-series trade-offs
High-volume metrics/log ingestion & query pipeline

Prep — high priority

Clean coding (Python/Go) incl. take-home quality (unit tests, docs, formatting)
DB-type trade-offs (SQL/NoSQL/time-series)
App architecture design

Prep — medium priority

SQL over metrics/log data
Time-series storage
Behavioral

Rejection patterns

Weak take-home (no tests/docs/formatting)
Cannot reason about DB-type trade-offs
Shallow coding under added complexity

Reported experiences

Data Engineer

ReportedFirst-hand

Recruiter (behavioral) → 2-engineer coding (Coderpad, 2 simple exercises) → take-home (code, docs, unit tests, formatting) → design (architecture for an app).

Rehearse for Datadog

Answered questions on what Datadog 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