Data + infrastructure
Healthcare data platforms, wearable streams, lakehouse models, orchestration, observability, and cloud migration work behind live product and analytics systems.
Unified wearable event streams and operational feeds into one governed platform.
Moved sensitive analytics data through Bronze, Silver, and Gold model layers.
Modernized batch and streaming workflows while preserving reporting continuity.
Work covered
The through-line is production ownership: ingestion contracts, modeling boundaries, freshness checks, monitoring, and cutover safety.
Streaming + ingestion systems
Design event-driven ingestion that keeps real-time and scheduled sources inside one platform contract.
Kafka, ingestion contracts, replay safety
Lakehouse + modeling
Shape Bronze/Silver/Gold and Delta-backed pipelines so raw inputs become trusted analytical tables.
Delta Lake, Spark, governed promotion
Orchestration + observability
Build for freshness, backfills, dependencies, and on-call clarity instead of shipping opaque DAGs.
Airflow, SLAs, monitoring, runbooks
Governance + compliance
Embed RBAC, lineage, auditability, and publishing gates where data changes state.
Healthcare-sensitive workflows
Modernization + migration
Move legacy pipelines without breaking reporting by using hybrid cutovers and validation paths.
Cloud migration, parallel runs, API modernization
Case studies
Role, scope, outcome, stack, and architecture for each system.
Unified wearable event streams and scheduled operational feeds into one governed platform for analytics and product reporting.
Role
Senior data engineer
Outcome
Platform footprint: One shared platform contract for streaming wearable and scheduled operational data.
Stack
Built a Bronze/Silver/Gold pipeline for clinical and wearable data so sensitive analytics could move from raw capture to decision-ready models with governance built in.
Role
Data engineer
Outcome
Trust model: Bronze/Silver/Gold became the standard promotion path for sensitive analytics data.
Stack
Modernized fragmented legacy pipelines into a cloud-oriented data platform without breaking reporting during the transition.
Role
Data engineer
Outcome
Migration safety: Legacy and modern flows ran in parallel during cutover so reporting continuity was protected.
Stack