EY is seeking a hands-on Cognite Data Engineering Lead to join our team. This role focuses on implementing robust data pipelines, industrial data models, and contextualization workflows within Cognite Data Fusion (CDF). You will be instrumental in ensuring implementation quality, making critical engineering decisions, and delivering reliable data for operational and analytical applications.

This is an opportunity to build a unique career within a global organization that values your perspective. Join us in shaping the future of industrial data and analytics.

Key responsibilities include configuring Cognite extractors and custom integrations, building resilient data loading mechanisms with checkpoints and recovery, and implementing repeatable writes with reconciliation checks. You will also develop and implement data models, including spaces, containers, and views, and leverage SQL and Python for data cleansing, transformation, and enrichment. Contextualization involves identifier-based matching and connecting operational and engineering records, while ensuring data provenance and relationships are maintained.

Additional duties involve writing and reviewing production code with automated tests, managing configuration in Git, automating deployments, and monitoring pipeline execution. Delivering documented consumer views, example queries, and runbooks, while identifying potential design changes, are also crucial aspects of this role.

We are looking for candidates with substantial hands-on experience in Cognite Data Fusion implementation and a strong understanding of production operations. Essential qualifications include proficiency in Python and SQL, with experience using the Cognite SDK and REST API. You should possess practical knowledge of Git, automated testing, CI/CD, and deployment configuration, along with familiarity with authentication and integration services within CDF.

Candidates should have strong implementation knowledge in at least one upstream or downstream setting, demonstrating the ability to apply relevant concepts across domains. Evidence of personally delivering and troubleshooting data pipelines used by operational or engineering applications is required. A relevant engineering, computer science, or information systems degree, or equivalent technical experience, is necessary.