data engineer for investment data

BIT Capital combines asset management expertise with the agility of a young tech company. Its investment products enable investors to participate in the growth of the technology-driven world. The company identifies emerging technology trends and investment opportunities on behalf of its investors.

• Build and improve internal products used by investment and research teams, including data pipelines, datasets, APIs, and tools
• Gather, triage, and prioritize requests from internal teams, maintain product integrity and consistency, and explain features and implementation trade-offs in non-technical language
• Design, build, and operate reliable data pipelines, ETLs, and integrations for complex financial and alternative datasets
• Reinforce engineering practices, including automated testing, CI/CD, pull requests, code reviews, and documentation
• Contribute to operational excellence through observability, monitoring, data quality, and secure data handling
• Use agentic AI for coding, review, testing, debugging, and documentation, and help advance these practices within the team

• 3–5 Years of professional software or data engineering experience building and operating production systems
• Strong Python and SQL skills, clean and readable code, and a good understanding of data modelling
• Experience writing unit, integration, and data-quality tests; working with CI/CD; and using pull requests and code reviews
• Ability to explain technical work, its purpose, and limitations to non-engineers, and to say no constructively
• Hands-on use of AI across engineering functions, including coding systems such as Claude Code and Codex
• Genuine interest in investing, financial markets, and data, and ambition to develop in these areas
• Builder mindset, pragmatic trade-offs, attention to quality, and ability to work quickly without being careless
• Care for correctness, reliability, security, and long-term maintainability; ability to improve codebases and teams
• Curiosity about investment decisions and the data behind them
• Clear communication about progress, trade-offs, and risks, with end-to-end ownership of problems
• Candidates who prefer fully specified tickets and no user contact may not be suited to this role
• Candidates who use AI tools only occasionally may not be suited to this role
• Будет плюсом: Databricks, PySpark, orchestration tools such as Airflow or Dagster, lakehouse formats such as Parquet, Delta, or Iceberg, AWS, investment or market data experience, internal-tool frontends or APIs, production LLM systems

• Work with petabytes of data and advanced tools in and beyond asset management, with Day 1 ownership of varied work and a steep learning curve
• Join a team of around 40 professionals, including 20 financial-market experts, sector specialists, data engineers, and software developers; the team includes 15 nationalities
• Work alongside experts in European asset management and interact directly with management and investors
• Visa sponsorship and relocation support are available
• Competitive compensation package with a variable component tied to fund performance