Our client is a fast-growing fintech company building a modern cloud data platform supporting financial analytics, reporting, and AI use cases.
We're looking for a Data Engineer to work alongside the Lead Data Engineer, helping build reliable pipelines and transform complex financial data into useful, well-structured datasets.
This is an opportunity to strengthen your engineering skills, contribute to real production systems, and gradually take ownership of projects with guidance from experienced technical colleagues.
- Build and maintain data pipelines using Python and SQL, while developing your skills in PySpark and Databricks.
- Help ingest and process financial datasets, including company filings, market data, financial statements, news, and unstructured documents.
- Develop data transformations and structured datasets for analytics, reporting, AI workflows, and downstream products.
- Implement data quality checks and tests to improve accuracy, consistency, and traceability.
- Support pipeline monitoring, investigate issues, and contribute to improvements in reliability and performance.
- Work with the Lead Data Engineer to translate requirements into practical, maintainable solutions.
- Participate in code reviews, document your work, and apply engineering best practices.
- Collaborate with Data, AI, Finance, and Engineering colleagues to understand how your work supports the product.
- 5+years of experience in Data Engineering or a related software or analytics engineering role. Relevant internships and apprenticeships will also be considered.
- Good foundations in Python and SQL, with experience applying them to practical data projects.
- An understanding of ETL/ELT, relational databases, and data modelling.
- Familiarity with Git and an interest in writing clean, testable, maintainable code.
- A structured approach to problem-solving and attention to data accuracy.
- Curiosity, a willingness to ask questions, and openness to feedback.
- The ability to collaborate effectively and adapt to evolving priorities.
- Exposure to Apache Spark, PySpark, or Databricks.
- Familiarity with a cloud environment such as GCP, AWS, or Azure.
- Experience with pipeline orchestration, automated testing, or monitoring.
- An interest in financial data, fintech, or AI applications.
- Previous experience in a startup or scale-up.
You don't need to have worked with every tool in our stack. We value strong fundamentals, practical experience, and the motivation to learn.
- Work closely with a Lead Data Engineer and learn through hands-on development, technical discussions, and code reviews.
- Contribute to production systems combining data engineering, financial analytics, and AI.
- See how the pipelines and datasets you build support the product and its users.
- Grow your technical skills and take on greater responsibility as you progress.
- Join a collaborative startup environment with a hybrid working setup in Paris.