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Joel Schwarzmann

Product & Engineering Leadership | Data Platforms | Developer Tooling
London, England, GB datajoely
Builder of developer tooling and data / AI platforms. Combines hands-on engineering with product leadership to deliver secure, interoperable systems. Skilled at translating complex technical concepts for diverse audiences. Passionate about creating platforms and products users love.

Experience

Director of Platform
2024-09-01 - 2025-08-31
Aneira Health
Leading development of a GCP-native data platform providing women with personalised, clinically validated healthcare insights that directly improve patient outcomes. Simultaneously building a world-class multimodal research platform integrating de-identified EHR, genomics, DICOM, and wearable data.
Technical Advisor
2024-09-01 - Present
Population Health Partners (incubator of Aneira Health)
Strategic and technical advisor across portfolio companies on data, ML, and AI platforms.
Principal Technical Product Manager
2021-01-01 - 2024-08-31
QuantumBlack Labs (a McKinsey company)
Product leader for developer tooling and durable ML delivery; stewarded open source and incubated multiple products end-to-end.
Senior Data Engineer
2017-02-28 - 2020-12-31
QuantumBlack (a McKinsey company)
Forward-deployed Data Engineer delivering production ML across pharma, manufacturing, insurance, and motorsport.
Senior Forensic Data Analyst
2013-09-01 - 2017-02-17
PwC
Delivered data-driven investigations into fraud, disputes, and trade surveillance; developed SQL/Python toolkits.

Education

BSc in Computer Science with Management
2010-09-01 - 2013-07-01
University of Birmingham
Grade: First Class (Hons)

Awards

Best Final Year Student
University of Birmingham, School of Computer Science

Skills

Languages: Python, SQL
Frameworks: Dagster, Airflow, dbt, sqlmesh, PySpark, Kedro, Ibis, Kafka, Pandera, FastMCP, FastAPI
Product & Practices: Product Management, Platform Strategy, MLOps, Telemetry & Privacy, DevEx, CI/CD, LLMs, Testing

Projects

Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help create data engineering and data science pipelines that are reproducible, maintainable, and modular.

Publications

medRxiv