Current focus across delivery, AI, and platform modernization
What I am actively driving now, where execution energy is concentrated, and which operating patterns are currently in motion.
Hands-on execution around multi-cloud programs, AI implementation, and platform operating models.
Current charter: Practice Lead - Multi Cloud Managed Services & Data Engineering at Confidential.
Current Role
Practice Lead - Multi Cloud Managed Services & Data Engineering
Active Tracks
3
Updated
February 17, 2026
Running migration and replatforming tracks across AWS, Azure, and GCP with controlled cutovers and reusable landing zone patterns.
- • 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.
- • Led cloud-to-cloud replatforming programs (AWS to Azure, AWS to GCP, and Azure to AWS) for analytics and DevOps stacks, with zero critical data-loss incidents during migration windows.
- • Implemented orchestration standards across MWAA (Airflow), Azure Data Factory, and Cloud Composer, governing 100+ DAGs/pipelines with SLA-aware alerting.
Translating GenAI and ML workloads into production workflows with deployment guardrails, monitoring, and platform consistency.
- • 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.
- • 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.
Improving delivery throughput, data quality, and production reliability through DataOps, CI/CD, observability, and SRE-aligned practices.
- • 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.
- • Led cloud-to-cloud replatforming programs (AWS to Azure, AWS to GCP, and Azure to AWS) for analytics and DevOps stacks, with zero critical data-loss incidents during migration windows.
- • Implemented orchestration standards across MWAA (Airflow), Azure Data Factory, and Cloud Composer, governing 100+ DAGs/pipelines with SLA-aware alerting.
Operating Cadence
- • 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%.
- • Ran delivery governance in Agile/SCRUM with quarterly OKRs, risk reviews, and stakeholder steering updates across multi-region programs.
Active Toolkit Mix
AWS Core
AWS Data & AI
Azure Core
Azure Data & AI
GCP Core
GCP Data & AI