About BIT Capital
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.
Responsibilities
•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
Requirements
•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
Nice to have
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
Conditions
•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
