Data Engineer

At Fadata, we take pride in our ability to adapt quickly to the ever-changing needs of our market and customers. This means that you, too, should be ready to embrace change as circumstances evolve. If you're excited about a fast-paced, dynamic environment where strategy is set but flexibility to grow with the market remains, then hit the apply button.

We have a bold vision: to become the global standard for core software solutions in the insurance industry. Our aim is to achieve this by developing a truly cloud-native solution that can scale limitlessly—and we want you to be part of that journey.

Our values—Trust, Openness, Passion, Innovation, and Care—serve as our guiding compass. They shape how we work every day, our relationships with clients, and the way we collaborate as a team.

Opportunity

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.

Curate, Catalog & Understand Our Data

Integration & Architecture

Build modern data assets & modern deliverable mechanisms

AI Knowledge-Base Pipelines

Toward Reportable Data & Analytics (Growth Area)

Collaboration

• 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.

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