Walmart Global Tech's Bangalore and Chennai data-engineering rounds probe Spark internals, SQL, and data-lake architecture at retail scale.
In 2026 expect a Spark round on joining a huge sales fact table against skewed dimensions (salting, broadcast joins, partition tuning), a SQL round on deduplicating inventory snapshots with window functions and making it incremental, and an architecture round on data-lake and warehouse layout for supply-chain analytics (partitioning, Parquet, serving layers). Walmart's data volumes are enormous, so interviewers reward engineers who reason precisely about shuffle cost, skew, incremental processing, and file layout rather than reaching for defaults. Concrete retail-scale reasoning wins.
About Walmart Global Tech
Walmart Global Tech Bengaluru (WGTB) is one of Walmart's largest tech hubs, working on supply chain systems, the walmart.com ecommerce platform, Sam's Club, Flipkart integration, and omnichannel checkout. Compensation includes WMT RSU: listed NYSE equity.
Recruiter screen and technical pre-screen
SQL and Spark coding round
Data-architecture and pipeline design round
Hiring-manager and behavioural round, then offer
Round 1 (45-60 min)
SQL round on window functions and deduplication.
Round 2 (60 min)
Spark round on joins, skew handling, and partition tuning.
Round 3 (45-60 min)
data-lake and warehouse architecture round.
Round 4 (45 min)
behavioural and hiring-manager round.
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Sign up free: unlock all questionsThe typical Walmart Global Tech recruitment process has 4 stages: Recruiter screen and technical pre-screen → SQL and Spark coding round → Data-architecture and pipeline design round → Hiring-manager and behavioural round, then offer.
Walmart Global Tech typically conducts 4 interview rounds: Round 1 (45-60 min): SQL round on window functions and deduplication.; Round 2 (60 min): Spark round on joins, skew handling, and partition tuning.; Round 3 (45-60 min): data-lake and warehouse architecture round.; Round 4 (45 min): behavioural and hiring-manager round..
HireStepX recommends the Scale-and-Skew framework for this type of interview: Reason about Spark shuffle and skew at retail scale, write incremental window-function SQL, and design partitioned lakehouse layouts with the right file formats
To answer this question well, HireStepX recommends the Scale-and-Skew approach: Reason about Spark shuffle and skew at retail scale, write incremental window-function SQL, and design partitioned lakehouse layouts with the right file formats Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Scale-and-Skew approach: Reason about Spark shuffle and skew at retail scale, write incremental window-function SQL, and design partitioned lakehouse layouts with the right file formats Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Scale-and-Skew approach: Reason about Spark shuffle and skew at retail scale, write incremental window-function SQL, and design partitioned lakehouse layouts with the right file formats Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Scale-and-Skew approach: Reason about Spark shuffle and skew at retail scale, write incremental window-function SQL, and design partitioned lakehouse layouts with the right file formats Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Scale-and-Skew approach: Reason about Spark shuffle and skew at retail scale, write incremental window-function SQL, and design partitioned lakehouse layouts with the right file formats Ground your answer in a specific real example from your own experience.