About the role Teza's Data Platform team owns the data the firm trades on: every backtest, every live strategy, every portfolio decision starts with data we ingested, cleaned, stored, and served. The scale, in plain numbers: • 1 PB of raw historical vendor data, growing by ~150 GB every day • 120 TB of processed, query-ready data in historical storage • Thousands of scheduled jobs: run by cron today, actively migrating to Apache Airflow • Alternative data delivered directly into the real-time feeds of live trading strategies This is a hands-on position on a small team of data engineers with growth potential. The firm is looking for outstanding technical skills, strong attention to detail, and a desire to architect and build data platforms. Location Austin, TX / Yerevan, Armenia (in-office requirement) Key Responsibilities • Work directly with Portfolio Managers and Quantitative Developers: turn their requirements into datasets and pipelines, and be the person who knows every nuance of the data they trade on. • Design and onboard new data sources into our warehouse; improve the robustness, speed, and scalability of our systems; manage data entitlements. • Build automated systems for data cleansing, anomaly detection, monitoring, and alerting, bad data must never reach a strategy. • Evaluate new tools and technologies for organizing, querying, and streaming large datasets, and when nothing on the market fits, build it. That's how the bitemporal store happened. • Support the production data warehouse the firm depends on. • Develop and maintain vendor relationships aligned with our business objectives. What we're building right now • A bitemporal data store, designed and written in-house from scratch. Every dataset answers both "what did we know then?" and "what do we know now?", which is what lets researchers trust a backtest. • A Python 3.14 migration of a large, long-lived codebase. • Adoption of the latest Apache Airflow: writing DAGs for the thousands of jobs moving off cron. • Pipelines for market data and alternative data: everything from exchange feeds to weather. • Real-time delivery: alternative data flows straight into strategies' live feeds. Pipelines you build sit in the trading path. • CI/CD for all of it, in GitHub Actions. Our Stack Python and Java · Apache Airflow · Slurm · NATS · PostgreSQL · MongoDB · S3 · NFS · GitHub Actions Basic Requirements • Proficiency in Python and Unix/Linux for data manipulation, scripting, and automation. • Strong SQL, including query optimization and performance tuning, and familiarity with NoSQL. • A solid grasp of data modeling: normalization and denormalization, and the judgment to know when each applies. Nice to have • Financial industry experience or internships. • Java (part of our platform is written in it). • Experience with on-premises data infrastructure. • Familiarity with a cloud platform (AWS or GCP). • Apache Airflow or similar workflow orchestration tools. Benefits • Health, visual and dental insurance • Flexible sick time policy