Practice Lead - Multi Cloud Managed Services & Data Engineering with Focus on AI Implementation

Built the Multi-Cloud Data & AI Engineering function from 0 to 8 engineers in the first two quarters and scaled to 18+ engineers across platform, data, and reliability tracks.

Owned 12+ migration waves from on-prem (Oracle, SQL Server, Hadoop, legacy ETL) to AWS/Azure/GCP, migrating 200+ TB and 1,000+ production jobs with controlled cutovers.

Designed and delivered 40+ Spark/PySpark pipelines across EMR, Glue, Dataproc, and Dataflow, processing 5+ TB/day with 60% faster batch completion.

Designed and delivered 40+ Spark/PySpark pipelines across EMR, Glue, Dataproc, and Dataflow, processing 5+ TB/day with 60% faster batch completion.

Implemented orchestration standards across MWAA (Airflow), Azure Data Factory, and Cloud Composer, governing 100+ DAGs/pipelines with SLA-aware alerting.

Implemented data quality gates with Great Expectations and custom PySpark checks, maintaining 99.9% data accuracy SLAs across business-critical datasets.

Introduced DataOps engineering practices (unit/integration/E2E tests, contract tests, release templates), improving pipeline reliability and reducing production incidents by 55%.

Executed FinOps optimization plans (spot compute, autoscaling, tiered storage, query tuning), reducing monthly platform costs by 40%.

Started with a lean DevOps pod and scaled it to 10+ engineers supporting multi-cloud platforms, release engineering, and SRE operations.

Reduced deployment lead time by 35% and rollback time by 50% through pipeline optimization, release templates, and progressive delivery controls.

Reduced deployment lead time by 35% and rollback time by 50% through pipeline optimization, release templates, and progressive delivery controls.

Built cloud-native data engineering foundations on AWS Glue, EMR, and S3 supporting 10+ TB/day for retail and e-commerce analytics workloads.

Operationalized Airflow on MWAA and hybrid schedulers, managing 50+ DAGs with automated retry, dependency controls, and incident routing.

Implemented S3 data lake zoning, lifecycle, and archival controls to improve governance and reduce storage cost by 30%.

Established dimensional modeling standards (Star/Snowflake/SCD2) that improved BI query performance for 500+ users.

Maintained 99.99% uptime using Prometheus, Azure Monitor, Grafana, and SLO/SLA dashboards with proactive remediation automation.