We are looking for Data Engineers for the team of our Fortune 50 client, building an agentic system acting across large-scale enterprise data. The main objective of the project is to expose enterprise data into a Neo4j-backed semantic graph optimized for agentic reasoning.
This is a remote-first position for engineers based in Europe, Turkey, and Middle East with a required overlap of US working hours (2-6 PM CET).
Built ETL pipelines from Microsoft Fabric Data Lake into Neo4j
Design data models.
Transform raw structured and unstructured data into clean, well-modeled graph inputs (nodes, edges, metadata).
Create Source to Target Mappings (STMs) for ETL specifications.
Implement automated ingestion patterns, incremental (delta) updates, and streaming/CDC workflows.
Gather requirements, set targets, define interface specifications, and conduct design sessions.
Work closely with data consumers to ensure proper integration.
Adapt and learn in a fast-paced project environment.
Start Date: ASAP
Location: Remote
Working hours: US time zone overlap required: 2-6pm CET
Long-term contract based-role: 6+month
Strong SQL skills for ETL, data modeling, and performance tuning.
Experience with Neo4j
Proficiency in Python, especially for handling and flattening complex JSON structures.
Hands-on experience with Microsoft Fabric, Synapse, ADF, or similar cloud data stacks.
Knowledge of Cypher, APOC, and graph modeling.
Familiarity with GraphRAG, retrieval systems, or RAG hybrids.
Understanding of software engineering and testing practices within an Agile environment.
Experience with Data as Code; version control, small and regular commits, unit tests, CI/CD, packaging, familiarity with containerization tools such as Docker (must have) and Kubernetes (plus).
Excellent teamwork and communication skills.
Proficiency in English, with strong written and verbal communication skills.
Efficient, high-performance data pipelines for real-time and batch data processing.
Knowledge of cryptography and its application in enterprise data modeling in regulated industries (Banking, Finance, Ops)
Semantic models, ontologies, or knowledge engineering
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