Senior Data Engineer

• 5+ years of experience in data engineering or backend engineering with data focus.
• Python. Strong programming skills; this is our primary stack.
• SQL and PL/SQL. Solid hands-on experience writing, reading, and refactoring database models and logic (Oracle).
• Hands-on experience with data pipeline tools (e.g., Airflow, Spark, Kafka, Flink).
• Experience with cloud data services (AWS, Azure, or GCP).
• Familiarity with ETL/ELT processes and data integration patterns.
• Knowledge of distributed systems and event-driven architectures.
• RAG / vector fundamentals. understanding of embeddings, vector stores, and retrieval concepts (or a clear aptitude to pick them up).
• Engineering discipline. Version control, testing, CI/CD awareness, and clean documentation.
• Mindset. Strong problem-solving and analytical skills, and comfort bringing order to legacy systems of unknown status.

• Governed environments — Experience working closely with Data Architects or within governed data environments.
• Data lakes & warehousing — basics of analytical data platforms and dimensional modelling.
• Data science basics — enough to collaborate on insights and analytics use cases.
• Data governance & cataloguing tools — lineage, cataloguing, and metadata management.
• Distributed / event-driven architectures and pipeline tooling (e.g. Airflow, Spark, Kafka).
• Enterprise or insurance domain — exposure to complex enterprise systems, insurance, or financial services.

• Become part of a fast-growing insurance software vendor with multinational teams across Europe – we actually have+15 nationalities in our company
• Receive a competitive remuneration package together with great opportunities for personal and professional growth
• A positive company culture built around people's wellbeing
• No meetings time when we can focus on our main tasks
• Work in a dynamic environment with an exceptional team that is always ready to help
• A half-day off the second Friday of the month, so you focus on passion projects, boost your creativity or spend time with your loved ones

If you are interested in this role, we will be glad to hear from you.

We are looking for a Data Engineer to design, build, and scale our data platform as a core enabler of Fadata’s AI and Data strategy. 

In this role, you will be responsible for developing robust data pipelines, ensuring high-quality and accessible data, and supporting advanced data use cases. You will work closely with Data Architects and Subject Matter Experts (SMEs) to ensure data solutions are aligned with business domains, architecture standards, and enterprise data strategy. 

This position is critical in bridging operational systems (including legacy and microservices architectures) with modern data platforms, as our engineering teams are transforming core systems into cloud-native architectures.

• Document and curate our current data assets — building a governed, trustworthy map of the estate that is usable across the company.
• Profile data by domain and version: what tables and columns exist, what is actually used, and where there is redundancy or refactor potential.
• Help classify and protect PII and sensitive data before any change is made.
• Contribute to data quality, validation, lineage, and governance practices.
• Integrate data solutions with microservices and event-driven architectures.
• Collaborate with Data Architect to evolve the data platform and architecture roadmap, aligned with cloud-native, AI enabled and SaaS transformation initiatives
• Work with APIs, messaging systems, and distributed data processing frameworks.
• Understand, document and enhance our current data deployments
• Get the best from our liquibase setup
• Understand our current data model, working domain by domain with SMEs.
• Support the design of the reporting & data-sharing data models and pipelines
• Apply columnar data formats and analytical data frameworks where they add value
• Design, build, and maintain Retrieval-Augmented Generation (RAG) pipelines and architectures.
• Implement data ingestion, chunking, embedding, indexing, and retrieval mechanisms.
• Work with vector databases and search technologies to enable trustworthy semantic retrieval for our agents.
• Ensure data used in AI systems is accurate, relevant, and up to date.
• Collaborate with Data Architect and SMEs to define knowledge boundaries and context relevance.
• Help us build a path toward more “reportable” data — applying ETL/ELT to move from operational data toward aggregated, analysis-ready datasets.
• Contribute to a reporting/aggregation layer and, over time, data lake and warehouse foundations.
• Work cross-functionally with Engineering, Architecture, Product, and Business teams.
• Contribute to best practices, standards, and documentation across the data function.