I'm a Data Engineer III focused on building reliable, scalable data platforms and production pipelines for biotech, diagnostics, and high-trust analytical workflows.

My work sits at the intersection of data engineering, cloud infrastructure, distributed data processing, production analytics, and ML-adjacent systems. I enjoy designing systems that can move data from messy operational sources into clean, governed, and reusable data products.

🌐 Website: jowinjestine.github.io

• Production data pipelines for API, file, database, and event-driven ingestion
• Incremental and idempotent ingestion systems that reduce duplicate processing and improve reliability
• Data models that support traceability, auditability, lineage, and operational reporting
• Batch and near-real-time workflows for internal platforms and scientific operations
• Cloud-native pipelines using Azure storage, functions, events, queues, and PostgreSQL
• Internal data tools and services using Python, SQL, FastAPI, Streamlit, R Shiny, and Docker
• ML-ready datasets, model input pipelines, prediction output tracking, and reproducible reporting flows

I care about building systems that are:

• Reliable — resilient to retries, late data, bad payloads, and partial failures
• Scalable — designed for growing data volume, more users, and more downstream consumers
• Observable — logged, monitored, and easy to debug when something breaks
• Governed — traceable from source system to transformed data product
• Maintainable — clear schemas, modular pipelines, and practical documentation
• Useful — built around real business, scientific, and operational workflows

• High-usage data pipelines and platform engineering
• API ingestion, webhook reconciliation, and change-data tracking
• Distributed and cloud-based data processing
• Data lake and warehouse architecture
• PostgreSQL schema design and analytical data modeling
• Data quality, lineage, observability, and audit logging
• Production analytics and ML-adjacent data systems
• Replacing spreadsheet-heavy workflows with durable data platforms

Languages: Python, SQL, R
Data Engineering: PostgreSQL, pandas, PySpark, DuckDB, dbt, SQLMesh
Cloud & Infra: Azure, Docker, Linux, GitHub, Bitbucket
Azure: ADLS Gen2, Azure Functions, Event Grid, Event Hub, Azure PostgreSQL
Apps & APIs: FastAPI, Streamlit, R Shiny, Plumber, Plotly
ML / Analytics: scikit-learn, XGBoost, PyTorch, statistical modeling
Orchestration: Mage AI, scheduled pipelines, event-driven workflows

I am especially interested in data platform work where pipelines are treated as production systems, not one-off scripts. That includes clear contracts, schema evolution, validation, logging, retries, backfills, monitoring, and well-defined ownership of data products.

A lot of my work involves connecting lab data, metadata, files, models, reports, and operational events through a clear data model so that teams can trust how data moves through the system.

• Incremental API ingestion and webhook reconciliation
• ELN, inventory, and operational data integration
• Sample, run, file, and report-level traceability
• Azure-based storage and event-driven processing
• PostgreSQL-backed analytical and operational systems
• Internal tools for scientific and business workflows
• Data quality checks, logging, monitoring, and audit trails
• ML-ready data generation and prediction reporting pipelines

• Website: jowinjestine.github.io
• GitHub: @jowinjestine
• Email: jowinjestine@gmail.com

Thanks for stopping by.