Job description

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

Requirements

Must-haves

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.
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.
Austin, TX / Yerevan, Armenia (in-office requirement)
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonci/cdjavasqlawsgcp

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental