Data Engineer – Fintech Startup

Mett; Paris; Hybride; EUR 65 000 - 90 000; Plein temps

Avantages offerts par ce poste

Hybrid work setup in Paris

Résumé du poste

Mett is seeking a Data Engineer to join a fast-growing fintech team in Paris. You will work with the Lead Data Engineer to build reliable data pipelines and transform complex financial data into well-structured datasets for analytics, reporting, and AI workflows.

Ideal candidates have 5+ years in data engineering, strong Python/SQL skills, and experience with PySpark/Databricks, cloud ecosystems, and data modelling. Hybrid Paris setup with opportunities to lead small projects over time.

Qualifications

• 5+ years of experience in Data Engineering or a related software/analytics engineering role.
• Strong foundations in Python and SQL with data projects.
• Understanding of ETL/ELT, relational databases, and data modelling.
• Familiarity with Git and writing clean, testable, maintainable code.
• Exposure to Apache Spark, PySpark, or Databricks.
• Experience with cloud environments such as GCP, AWS, or Azure.
• Experience with pipeline orchestration, automated testing, or monitoring.

Responsabilités

• Build and maintain data pipelines using Python and SQL, while developing skills in PySpark and Databricks.
• Ingest and process financial datasets including 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 reliability and performance improvements.
• Collaborate with Lead Data Engineer to translate requirements into practical, maintainable solutions.
• Participate in code reviews, document work, and apply engineering best practices.
• Collaborate with Data, AI, Finance, and Engineering teams to understand product support needs.

Connaissances

Python

SQL

PySpark/Databricks

ETL/ELT

Data modeling

Git

Cloud (GCP/AWS/Azure)

Spark

Data pipelines

Outils

Databricks

Git

Description du poste

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.