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About the role
Keytag is looking for a strong data engineer to own how data gets captured, moved, and shaped and then turned into a knowledge graph that the rest of the business can actually use. This is a design and build role. You'll be designing ontology and pipelines from scratch, deciding how varied source data becomes clean, connected, queryable information. What you'll do Build data ingestion pipelines in Python, orchestrated with Airflow working out how to capture data reliably from a range of varied sources Extend our AI data platform, the backbone of our Agent Framework Model that data as a knowledge graph entities, relationships, the structure that makes it useful (we use Neo4j) Transform and serve data as JSON in Redis for fast downstream access Explain your thinking clearly to both technical and non-technical people What we're looking for Solid Python real engineering, not just scripting Hands-on Airflow experience writing new DAGs and tasks from scratch, not just maintaining existing ones (DAG design, backfills, idempotency, managing dependencies) Experience with Neo4j, Cypher, or knowledge graphs generally Good instincts for data modelling you can look at source data and reason about how to structure it A clear communicator who can talk through a design and defend it Someone who can balance speed of delivery against good design, and work iteratively from blank page to useful to finished Benefits Work with talented engineers building AI-powered products that large organizations rely on. Friendly and well-organized team with an open communication style. Room to keep growing, both your technical and your softer skills Remote work, providing flexibility and work-life balance. Medical insurance coverage for you and your family. Keytag is looking for a strong data engineer to own how data gets captured, moved, and shaped and then turned into a knowledge graph that the rest of the business can actually use. This is a design and build role. You'll be designing ontology and pipelines from scratch, deciding how varied source data becomes clean, connected, queryable information. What you'll do Build data ingestion pipelines in Python, orchestrated with Airflow working out how to capture data reliably from a range of varied sources Extend our AI data platform, the backbone of our Agent Framework Model that data as a knowledge graph entities, relationships, the structure that makes it useful (we use Neo4j) Transform and serve data as JSON in Redis for fast downstream access Explain your thinking clearly to both technical and non-technical people What we're looking for Solid Python real engineering, not just scripting Hands-on Airflow experience writing new DAGs and tasks from scratch, not just maintaining existing ones (DAG design, backfills, idempotency, managing dependencies) Experience with Neo4j, Cypher, or knowledge graphs generally Good instincts for data modelling you can look at source data and reason about how to structure it A clear communicator who can talk through a design and defend it Someone who can balance speed of delivery against good design, and work iteratively from blank page to useful to finished Benefits Work with talented engineers building AI-powered products that large organizations rely on. Friendly and well-organized team with an open communication style. Room to keep growing, both your technical and your softer skills Remote work, providing flexibility and work-life balance. Medical insurance coverage for you and your family.