Data Engineer

Data Engineer — Extract Team (Remote, Eastern Europe / LatAm)

Who are we? We are a quantitative investment firm based in San Francisco. We automate investing decisions based on business fundamentals, combining data science, machine learning, and disciplined engineering practice. We run a market-neutral long/short equity strategy driven by our own machine-learning platform.

We're a small team distributed across North America and Eastern Europe, built around two principles that shape how we work day to day: reason from first principles, and communicate openly and precisely.

What do we do? We manage a sizable data pipeline that feeds thousands of features into the models that automate our investing decisions. That pipeline moves data from a wide range of sources into several different environments, each supporting a different type of analysis — research, reporting, and live trading.

Why do we do it? Our investing decisions are only as good as the data behind them. Clean, well-aligned, leak-free data is what lets our models make sound calls at scale — so the pipeline isn't a support function, it's core infrastructure for the strategy itself.

How do we do it? We treat the data platform as a product: infrastructure as code, rigorous data quality checks, careful time-series alignment to prevent forward-looking leakage, and close collaboration between the people who build the pipeline and the people who depend on it.

Who works here? You'll join a small but very capable and motivated team of data scientists, engineers, and portfolio managers, working directly alongside our current data engineer.

Why is this role open? We're looking for an experienced Data Engineer to own and grow this pipeline as our data needs and modeling ambitions expand.

Who are we looking for? This is a pipeline and platform role — you won't be asked to build dashboards. You'll build and run the infrastructure that our reporting and our models depend on.

What professional skills matter most?

Required

• 5+ years of professional, hands-on data engineering experience
• Strong understanding of data warehouse principles
• Strong production experience with Python, Snowflake (or another modern columnar database), Airflow, and DBT
• Hands-on experience with a cloud service provider, ideally AWS — especially S3 and Athena
• Infrastructure as Code experience: Terraform, CloudFormation, Pulumi, or equivalent
• Familiarity with common software engineering practices: Git and GitHub, pull requests and code reviews, unit and integration tests, continuous integration
• Extreme attention to detail and a passion for digging into data quality and consistency from a business domain perspective
• Excellent verbal and written communication in English

Nice to have

• Experience in financial services, especially equity investing
• Understanding of equity investing, fundamental analysis, and the data that supports it
• Data quality, lineage, and pipeline observability tooling
• Russian language skills

What will you do in the project?

Pipeline Ownership

• Own, operate, and improve the data pipeline that moves data from various sources into several environments for different types of analysis
• Incorporate new data sources with proper time-series alignment to ensure correct data exposure and prevent forward data leakage

Reporting Infrastructure

• Set up and run the reporting pipeline infrastructure behind P&L, returns, and exposure reporting (sector, industry, risk factor, and others)

Platform & Infrastructure

• Manage the cloud infrastructure of the data platform as code
• Guard data quality and consistency, and troubleshoot complex data issues end to end

Team Support

• Support the various data science and machine learning teams
• Lend development and operational support to the trading execution infrastructure
• Understand high-level business objectives related to equity investing, and deliver data products according to team priorities

What do we offer?

• Remote work from Eastern Europe (preferred) or LatAm
• Daily overlap with the US team from 6:00–9:00 AM Pacific Time (15:00–18:00 CEST / 16:00–19:00 EEST)
• Compensation from USD 7,000/month, higher for exceptional candidates
• Performance bonus of 25–50%, based on firm and individual performance
• B2B contract, paid through Deel

Note: we are not able to contract with candidates residing in Russia.

Hiring Process

• Introductory call
• Intro call with the hiring manager
• Test problem
• Technical interview
• Final interview with leadership

. Skillset Required: Data Engineering, Data Pipelines, Data Warehousing, Python, Snowflake, Airflow, DBT, AWS, S3, Athena, Infrastructure as Code, Terraform, CloudFormation, Pulumi, Git, GitHub, Pull Requests, Code Reviews, Unit Testing, Integration Testing, Continuous Integration, Attention to Detail, Data Quality, Data Consistency, Business Domain Perspective, Verbal Communication, Written Communication, Data Science, Machine Learning, Financial Services, Equity Investing, Fundamental Analysis, Data Lineage, Pipeline Observability, Time-Series Alignment, Data Leakage Prevention, Reporting Infrastructure, P&L Analysis, Returns Analysis, Exposure Reporting, Sector Reporting, Industry Reporting, Risk Factor Reporting, Cloud Infrastructure, Troubleshooting, Trading Execution Infrastructure, Data Products, Data Integration, Collaboration