data engineer for investment data

BIT Capital GmbH; Berlin; On-site; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Visa sponsorship and relocation

Job summary

BIT Capital GmbH ищет инженера по данным/софтверного инженера для разработки и поддержки продакшн-систем в инвестиционных командах. Вы будете строить пайплайны данных, API и инструменты, сотрудничать с исследовательскими и инвестиционными командами.

Требуется 3–5 лет опыта, сильные навыки Python/SQL, опыт работы с CI/CD и тестированием качества данных. Применяются современные инструменты и ИИ в процессе разработки.

Qualifications

• 3–5 лет опыта работы над продакшн-системами в софтверной/датной инженерии.
• Сильные навыки Python и SQL, чистый код и хорошее моделирование данных.
• Опыт написания модульных, интеграционных и тестов качества данных; CI/CD; ревью кода.
• Умение объяснить техническое значение коллегам не инженерам; конструктивно говорить «нет».
• Опыт использования ИИ в инженерии, включая кодирование систем (Claude Code, Codex).

Responsibilities

• Строить и улучшать внутренние продукты для команд по инвестициям и исследованию: пайплайны, датасеты, API.
• Собирать и приоритезировать запросы, поддерживать целостность и объяснять trade-offs не-техникам.
• Проектировать и эксплуатировать надежные конвейеры данных и интеграции для фин. и альтернативных датасетов.
• Продвигать инженерные практики: тестирование, CI/CD, PR, код-ревью, документация.
• Повышать операционную эффективность через наблюдаемость, качество данных и безопасность.
• Использовать агентный ИИ для кодирования, обзора, тестирования и документации; развивать практики в команде.

Skills

Python

SQL

Data pipelines

Data modeling

CI/CD

Tools

Databricks

PySpark

Airflow

Dagster

Job description

Описание:

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.

Задачи:

• 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

Требования:

• 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

Условия:

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