Data Engineer

Reposted One Month Ago
2 Locations
In-Office
Mid level
Financial Services
The Role
Build and maintain data platform and production data warehouse; translate PM and quant requirements into technical solutions; design ETL, data modeling, cleansing, monitoring, and alerting systems; evaluate new tooling; optimize performance and manage vendor relationships.
Summary Generated by Built In

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

Skills Required

  • Proficiency in Python for data manipulation, scripting, and automation.
  • Proficiency in Unix/Linux for scripting and automation.
  • Strong SQL knowledge, including query optimization and performance tuning.
  • Familiarity with NoSQL databases (ideally Postgres and MongoDB).
  • Strong understanding of data modeling principles, including normalization and denormalization techniques.
  • Familiarity with cloud platforms (e.g., AWS or GCP).
  • Experience with Git version control, collaborative workflows (e.g., GitHub), and CI/CD best practices.
  • Experience architecting and building data platforms; hands-on data engineering experience.
  • Bachelor's degree in Computer Science, Information Technology or related field.
  • Financial industry internships / experience.
  • Experience with Java.
  • Experience with on-premises data infrastructure (e.g., Hadoop).
  • Experience with Apache Airflow or similar workflow orchestration tools.
  • Master's degree in Computer Science, Information Technology, Data Science or related field.
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The Company
HQ: Chicago, IL
79 Employees
Year Founded: 2009

What We Do

Teza Technologies is an innovative quantitative asset management firm founded in 2009 by high-frequency trading expert Misha Malyshev. Our multi-strategy, multi-PM platform is founded on microstructure data and signals. Quantitatively-informed digital assets strategies complement our core global futures and stat arb strategies. We pride ourselves on attracting and retaining top talent, developing strategies with a data-driven and science-backed methodology, and continuously innovating in pursuit of alpha for our clients. Our 70 employees are distributed across offices in Austin, New York, Chicago, and Shanghai. phone: 312.768.1600 inquiries: [email protected] candidates: [email protected]

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