Data Engineer

Teza Technologies; Austin (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health, vision and dental insurance

Flexible sick time policy

Job summary

Teza Technologies is seeking a hands-on Data Engineer to build and maintain a bitemporal data platform that powers trading strategies. You will collaborate with Portfolio Managers and Quant Developers to produce data pipelines, source new datasets, and ensure data quality and lineage.

In Austin, TX (in‑office) you’ll migrate pipelines to Airflow, optimize storage, and scale the data warehouse. The role emphasizes robust automation, tool evaluation, and close vendor relationships.

Qualifications

• Proficiency in Python and Unix/Linux for data manipulation, scripting and automation.
• Strong SQL, including performance tuning, and familiarity with NoSQL databases.
• Solid grasp of data modeling: normalization vs. denormalization and when to apply each.

Responsibilities

• Turn requirements from Portfolio Managers and Quant Developers into datasets and pipelines, owning data nuance.
• Design and onboard new data sources into the warehouse; improve robustness, speed, and scalability; manage data entitlements.
• Build automated systems for data cleansing, anomaly detection, monitoring, and alerting; ensure bad data never reaches a strategy.

Skills

Python

Unix/Linux

SQL

NoSQL

Data modeling

Tools

Apache Airflow

PostgreSQL

MongoDB

GitHub Actions

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