Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Situation: I've built data platforms across AWS, Azure, GCP. Task: Deliver scalable, cost-efficient data solutions. Action: AWS—S3, Glue, Athena, EMR, Redshift; Step Functions + Lambda. Azure—ADF, Synapse, Databricks, Event Hub. GCP—BigQuery, Dataflow, Composer. Common patterns:...
Situation: I've built data platforms across AWS, Azure, GCP. Task: Deliver scalable, cost-efficient data solutions. Action: AWS—S3, Glue, Athena, EMR, Redshift; Step Functions + Lambda. Azure—ADF, Synapse, Databricks, Event Hub. GCP—BigQuery, Dataflow, Composer. Common patterns: Lakehouse, medallion layers, IaC (Terraform), CI/CD. Result: Migrated 3 orgs to cloud; reduced infra cost 25% via right-sizing and lifecycle. Best practice: Managed services; partitioning; columnar formats; security and cost governance.
Red Flag: Claiming expertise in all three without depth in any. Pro-Move: 'I've led migrations on all three—AWS for current role (Glue, S3, Redshift); Azure at prior (ADF, Synapse); GCP for BigQuery—here's the trade-offs I've seen.'
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