Senior Data Engineer

We are looking for aSenior Data / Ingestion Engineerfor our client, a consultancy that helpsbanks and insurerstransform their operations through AI, automation, and advanced analytics, with a strong focus onAnti-Financial Crime(fraud prevention, AML, KYC) andenterprise AI platforms. You will join their delivery for aDutch insurance client.

We're looking for an engineer with deep experience buildingrobust ingestion pipelines for unstructured documentsand integratingOCR and document extractiontechnologies. 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 along-term remote-first contract positionfor candidates based in Europe.

Responsibilities
* Design and implement scalable pipelines for ingesting high-volume unstructured insurance documents (PDFs, scans, emails, Word, Excel, PowerPoint).
Design and implement scalable pipelines for ingesting high-volume unstructured insurance documents (PDFs, scans, emails, Word, Excel, PowerPoint).
* Build connectors to document sources such as SharePoint and email.
Build connectors to document sources such as SharePoint and email.
* Integrate, configure, and optimise OCR and document parsing technologies to extract high-accuracy text and layouts.
Integrate, configure, and optimise OCR and document parsing technologies to extract high-accuracy text and layouts.
* Build automated workflows for text cleaning, normalisation, semantic chunking, and metadata tagging.
Build automated workflows for text cleaning, normalisation, semantic chunking, and metadata tagging.
* Design vector storage schemas and robust retrieval mechanisms (RAG) to feed downstream AI models.
Design vector storage schemas and robust retrieval mechanisms (RAG) to feed downstream AI models.
* Ensure document processing pipelines meet enterprise security and low-latency SLA requirements.
Ensure document processing pipelines meet enterprise security and low-latency SLA requirements.
* Build automated error monitoring and extraction validation loops that flag low-confidence OCR outputs.
Build automated error monitoring and extraction validation loops that flag low-confidence OCR outputs.
* Apply engineering best practices across the pipeline lifecycle: version control, CI/CD, and testing.
Apply engineering best practices across the pipeline lifecycle: version control, CI/CD, and testing.

Work Conditions
* Start Date: ASAP
Start Date: ASAP
* Location: Remote within Europe (CEE preferred)
Location: Remote within Europe (CEE preferred)
* Long-term contract-based role:until July 2027 with possible extension
Long-term contract-based role:until July 2027 with possible extension
* Contract with EU LCC

Contract with EU LCC

Requirements
* 5–10 years' experience in data engineering.
5–10 years' experience in data engineering.
* Proven experience building data processing and document ingestion pipelines on public cloud platforms.
Proven experience building data processing and document ingestion pipelines on public cloud platforms.
* Hands-on experience processing unstructured documents (PDF, Word, Excel, PowerPoint, scans, emails).
Hands-on experience processing unstructured documents (PDF, Word, Excel, PowerPoint, scans, emails).
* Experience building connectors to enterprise sources such as SharePoint and email.
Experience building connectors to enterprise sources such as SharePoint and email.
* Practical experience with document extraction / OCR tools, e.g. AWS Textract or equivalent.
Practical experience with document extraction / OCR tools, e.g. AWS Textract or equivalent.
* Strong engineering practices: Git, CI/CD, automated testing.
Strong engineering practices: Git, CI/CD, automated testing.
* Experience in banking or insurance is an advantage.
Experience in banking or insurance is an advantage.

Technical Requirements

Mandatory
* Python, SQL

Python, SQL
* AWS: S3, Step Functions, CloudWatch

AWS: S3, Step Functions, CloudWatch
* OCR / document extraction: AWS Textract or equivalent
OCR / document extraction: AWS Textract or equivalent
* Unstructured document processing and ingestion pipelines
Unstructured document processing and ingestion pipelines
* Git, CI/CD, testing

Git, CI/CD, testing

Nice to have
* Vector databases and RAG architectures

Vector databases and RAG architectures
* Azure, Databricks

Azure, Databricks
* Financial services / insurance domain experience
Financial services / insurance domain experience

Highlights

If you are passionate about turning unstructured documents into AI-ready data, and want to help leading financial institutions fight financial crime with AI, this role is for you!

Originally posted onHimalayas