01
Data Engineer @ BNY
New York, NY · Jan 2026 — Current
- Reduced data incidents 60% engineering automated quality monitoring with Python, Great Expectations, and Airflow plus dbt tests, enforced via GitHub Actions CI/CD across analytics and ML training workflows.
- Delivered 99%+ pipeline uptime on production ETL/ELT flows processing 10M+ daily records with Python, SQL, Airflow, and GCP Dataflow, powering executive BI and downstream ML feature pipelines.
- Engineered 50+ ML-ready features on Databricks + PySpark for fraud risk models, orchestrating with Dagster and training directly in BigQuery ML with reproducible lineage.
- Built real-time fraud detection infrastructure on Kafka + PySpark, delivering low-latency event pipelines scoring 10M+ daily transactions for deployed anomaly detection models.
- Engineered BigQuery semantic layers and aggregated models consumed by Power BI for fraud risk and executive KPI visibility, cutting ad-hoc engineering requests by 40%.
60%
Incidents ↓
99%+
Uptime
10M+
Daily Records
50+
ML Features
40%
Ad-hoc ↓
