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Big Tech & Enterprise Software

Salesforce

Enterprise SaaS / CRM · Data Engineering interview

SQL + Python pairing and a strong system-design round with explicit prompts (10TB-in-5-minutes, GDPR-deletion data lake).

Cloud / platform

AWS + own infra; Spark, Kafka/Flink, data lake

Reported difficulty

Not reported

SQLPythonSparkHadoopHive

Hiring process

Recruiter

Technical screen (SQL + algo)

Onsite (3–5)

System design

Behavioral

Offer

Round-by-round breakdown

SQL

Medium confidence

Joins, window functions, deduplication
Incremental transformations, cohort-style queries
Performance awareness

Python / Coding

Medium confidence

Data structures, file processing, APIs, parsing
Pipeline-style transformations; basic LeetCode

System Design

Medium confidence

Architect a system to ingest 10TB of log data daily, queryable within 5 minutes
Design a data lake supporting GDPR deletion + historical trend analysis
Idempotency, exactly-once, disaster recovery

System & pipeline design prompts

10TB logs/day queryable within 5 minutes
GDPR-deletion + historical-analysis data lake
Batch vs stream; NoSQL vs Relational vs Data Lake; skew/partitioning/backfill

Prep — high priority

SQL (window, dedup, cohort, incremental, perf)
System design (batch vs stream, storage choice, idempotency/exactly-once/DR)
Python pipeline transformations

Prep — medium priority

Spark/Hadoop/Hive
Salesforce values behavioral

Rejection patterns

Shallow system design (no idempotency/exactly-once/DR/storage trade-offs)
Weak SQL cohort/incremental
Thin Spark

Reported experiences

Data Engineer

IllustrativeComposite

Composite: recruiter → technical screen (SQL + algo) → onsite (SQL dedup/cohort, Python pairing, system design 10TB-in-5-min / GDPR data lake, behavioral).

Rehearse for Salesforce

Answered questions on what Salesforce 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…

Big Tech & Enterprise Software

Salesforce

Enterprise SaaS / CRM · Data Engineering interview

SQL + Python pairing and a strong system-design round with explicit prompts (10TB-in-5-minutes, GDPR-deletion data lake).

Cloud / platform

AWS + own infra; Spark, Kafka/Flink, data lake

Reported difficulty

Not reported

SQLPythonSparkHadoopHive

Hiring process

Recruiter

Technical screen (SQL + algo)

Onsite (3–5)

System design

Behavioral

Offer

Round-by-round breakdown

SQL

Medium confidence

Joins, window functions, deduplication
Incremental transformations, cohort-style queries
Performance awareness

Python / Coding

Medium confidence

Data structures, file processing, APIs, parsing
Pipeline-style transformations; basic LeetCode

System Design

Medium confidence

Architect a system to ingest 10TB of log data daily, queryable within 5 minutes
Design a data lake supporting GDPR deletion + historical trend analysis
Idempotency, exactly-once, disaster recovery

System & pipeline design prompts

10TB logs/day queryable within 5 minutes
GDPR-deletion + historical-analysis data lake
Batch vs stream; NoSQL vs Relational vs Data Lake; skew/partitioning/backfill

Prep — high priority

SQL (window, dedup, cohort, incremental, perf)
System design (batch vs stream, storage choice, idempotency/exactly-once/DR)
Python pipeline transformations

Prep — medium priority

Spark/Hadoop/Hive
Salesforce values behavioral

Rejection patterns

Shallow system design (no idempotency/exactly-once/DR/storage trade-offs)
Weak SQL cohort/incremental
Thin Spark

Reported experiences

Data Engineer

IllustrativeComposite

Composite: recruiter → technical screen (SQL + algo) → onsite (SQL dedup/cohort, Python pairing, system design 10TB-in-5-min / GDPR data lake, behavioral).

Rehearse for Salesforce

Answered questions on what Salesforce 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