I design and build data platforms as distributed systems - with clear contracts, strong observability, and production-grade reliability.
Currently leading a 300TB+ Lakehouse platform processing 1B+ CDC events/day across 70+ heterogeneous sources in a regulated banking environment.
- Architect Lakehouse platforms (Iceberg, Spark, Impala) at scale
- Build contract-driven data systems with schema governance & lineage
- Design reliable CDC ingestion (Kafka, Debezium, idempotency patterns)
- Treat data pipelines as production services (SLOs, monitoring, failure handling)
- Translate business problems into clear technical decisions (ADRs/RFCs)
- Clarity over complexity - if you can't explain it, you don't understand it
- Contracts over assumptions - schemas are APIs
- Observability by design - not as an afterthought
- Failure is expected - systems must survive it
Data Platform: Apache Iceberg, Spark, Impala, Flink, dbt, Kafka, Debezium, Oracle GoldenGate
Backend & Infra: Python, FastAPI, SQLAlchemy, Django · Airflow · Kubernetes, Docker, Terraform
Observability & Governance: Prometheus, Grafana · Data Contracts, Column-level lineage · Apache Ranger (RBAC/ABAC)
Cloud & Storage: AWS (S3, Athena, Glue, Redshift) · MinIO
Production-grade metadata & governance service controlling schema evolution, data contracts, and ingestion safety.
- Contract enforcement at ingestion layer
- Schema evolution with breaking-change detection
- Column-level lineage & metadata management
- Impact: onboarding from 3 days → 3 hours, zero breaking schema changes in production
Serverless ingestion + canonicalization pipeline that collects weekly music release data from Beatport, stores raw snapshots in S3, and normalizes into PostgreSQL.
- AWS Lambda, SQS, Aurora PostgreSQL Serverless, Terraform
- Async processing with idempotent canonicalization
- Infrastructure-as-Code, zero-server maintenance
In real-world data platforms - failure is not an edge case, it's the default.