We are looking for a Senior Data / Ingestion Engineer for our client, a consultancy that helps banks and insurers transform their operations through AI, automation, and advanced analytics, with a strong focus on Anti-Financial Crime (fraud prevention, AML, KYC) and enterprise AI platforms. You will join their delivery for a Dutch insurance client.
We're looking for an engineer with deep experience building robust ingestion pipelines for unstructured documents and integrating OCR and document extraction technologies. You will turn high volumes of insurance documents (PDFs, scans, emails, Office files) into structured, high-quality, AI-ready data that feeds downstream RAG and AI models.
This is a long-term remote-first contract position for candidates based in Europe.
Responsibilities
Inventory all internal and external data sources: what data comes in and out, how (API, database link, file) and under which contract.
Define the data contracts (canonical client data, screening result) and the data model for evidence, audit trail and reporting.
Build connectors to Moody's GRID, national registers (for Romania: trade register, beneficial-owner register, insolvency bulletin) and local UBO data providers (for example Coface).
Connect Databricks with legacy databases (mainly SQL Server) and core systems.
Apply engineering best practices across the pipeline lifecycle: version control, CI/CD, and testing.
Work Conditions
Start Date: ASAP
Location: Remote within the EU
Long-term contract-based role: until July 2027 with possible extension
Contract with EU LCC
5–10 years' experience in data engineering.
Proven experience building data processing and document ingestion pipelines on public cloud platforms.
Strong engineering practices: Git, CI/CD, automated testing.
Experience in banking or insurance is an advantage.
Technical Requirements
Mandatory
Python, SQL
Databricks (Delta Lake, Unity Catalog)
SQL Server and integration with legacy databases
Data modelling and data contracts
Git, CI/CD, testing
Nice to have
KYC/UBO data experience
Working with data providers like (Moody's, Dow Jones, LexisNexis, Coface)
Financial services / insurance domain experience