Engineer - Data & Platforms (m/f/d)

United States Digital Space LLC; Berlin; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

United States Digital Space LLC is seeking an Engineer - Data & Platforms to build and run data products for our investment teams in Berlin. You’ll own end-to-end data pipelines, datasets, APIs, and tooling within a Python/SQL stack on AWS and Databricks.

You’ll collaborate with Quant Research, AI Engineering, Product & Data, and the investment team to ship reliable data platforms while advancing agentic AI as a core capability.

Qualifikationen

• 3–5 years of professional software or data engineering experience.
• Strong Python and SQL with clean, readable code and data modeling.
• Experience with CI/CD, testing and code reviews.
• Able to explain what you built to non-engineers.
• Hands-on use of AI in engineering workflows.
• Interest in investing and financial data.

Aufgaben

• Build and improve internal data products: pipelines, datasets, APIs, and tools.
• Design, build, and operate reliable data pipelines and integrations.
• Promote engineering hygiene: tests, CI/CD, code reviews, documentation.
• Contribute to observability, data quality, and secure data handling.
• Use agentic AI across engineering tasks to improve workflow.

Kenntnisse

Python

SQL

CI/CD

Testing

Communication

AI integration

Tools

Databricks

Airflow

Dagster

Parquet

Delta Lake

AWS

Jobbeschreibung

Your Role & How we work

As our Engineer - Data & Platforms (m/f/d), you build and run the internal data products that our investment team, Quantitative Research and AI Engineering rely on every day: the pipelines, datasets, APIs and tools that turn a large and growing pool of financial and alternative data into something investors can act on. Our stack is Python, SQL, AWS, and Databricks.

You report to Anton Popov, Director of Engineering. This is a hands‑on engineering role: most of your time goes to designing, building, and shipping. The team is small, so you take ownership of real products early, and you get a manager and colleagues invested in your growth into investment and data‑platform topics. You work closely with Quantitative Research, AI Engineering, Product & Data, and the investment team, and we treat agentic AI as a default in how we build.

What you will do

Build and improve the internal products the investment and research teams use daily: data pipelines, datasets, APIs, and tooling, on top of our lakehouse and orchestration stack.

• Work directly with the teams you build for: take in their requests, triage and prioritise them, protect the integrity and consistency of the product, and explain features and implementation trade-offs in clear, non-technical language.
• Design, build, and operate reliable data pipelines, ETLs and integrations that handle complex financial and alternative datasets.
• Bring and reinforce good engineering habits in the team: automated testing at several levels, CI/CD, pull requests and code review, and clear documentation.
• Contribute to operational excellence: observability, monitoring, data quality, and secure data handling.
• Use agentic AI across engineering work (coding, review, testing, debugging, documentation) and help the team push these practices forward.

The experience you bring

3 to 5 years of professional software or data engineering experience, building and operating systems in production.

• Strong Python and strong SQL, with clean, readable code and a good sense for data modelling.
• Proven engineering discipline: you write tests (unit, integration, data quality), work with CI/CD, and use pull requests and code review as a matter of course, and you can show how you helped a team adopt them.
• Clear communication with non-engineers: you can explain what you built, why, and what it will and will not do, and you can say no constructively.
• Hands‑on use of AI across engineering functions, including frontier coding systems such as Claude Code and Codex, as a core part of how you work.
• Genuine interest in investing, financial markets and data, and the ambition to grow into these topics.
• Nice to have: Databricks or PySpark; data platform tooling such as orchestration (Airflow, Dagster or similar), lakehouse formats (Parquet, Delta, Iceberg) and AWS; experience with investment, financial or market data; building web frontends or APIs for internal tools; LLM systems in production.

2 reasons why you should not apply

You prefer fully specified tickets and no contact with users. Here the people you build for sit next to you, ask for things directly, and expect you to weigh in on what should be built and how.

• You treat AI tooling as an occasional helper. We expect agentic AI to be a default in how you build, review, test, and ship, every day, not now and then.

Your mindset

You are a builder who loves to ship: pragmatic on trade-offs, careful on quality. You move fast, but fast does not mean sloppy.

• You care about correctness, reliability, security, and long-term maintainability, and you leave codebases and teams better than you found them.
• You are curious about how investment decisions are made and want to understand the data behind them, not just move it.
• You see AI-powered development as a competitive advantage, not a novelty, and use agentic AI as a core part of how you work.
• You communicate clearly on progress, trade-offs, and risk, and you own problems end‑to‑end.

What we offer

Work with petabytes of data and the most cutting‑edge tools in and beyond asset management, with Day 1 ownership of challenging, varied work and a steep learning curve.

• Become part of a unique success story in European asset management, alongside the most knowledgeable experts in the industry on one of the most consequential investment themes of the next decade.
• A motivated, highly international team (15 nationalities) that gathers in person at our Berlin HQ and supports each other, with flat hierarchies and direct interaction with our management team and investors.
• Visa sponsorship, relocation support, and a competitive compensation package with a meaningful variable component tied to fund performance.