Data Engineering Lead

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

EY GDS | COGNITE DATA ENGINEERING LEAD

Oil & Gas | Manager

Job Title     Cognite Data Engineering Lead – Manager

    Experience Level        10+ Years

Job Description

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. We’re counting on your unique voice and perspective to help EY become even better. Join us and help build a better working world.

We are seeking a hands-on Cognite Data Engineering Lead to implement reliable data pipelines, industrial data models and contextualization workflows in Cognite Data Fusion (CDF). You will own implementation quality, make component-level engineering decisions within the agreed architecture, and deliver data that operational applications and analytical solutions can reliably consume.

Role Details

Key responsibilities include:

Extraction and ingestion

• Configure supported Cognite extractors and implement API or custom integrations where required.
• Build source-appropriate incremental loads with persistent checkpoints, retries and restart behaviour; handle pagination, throttling, historical backfills and late-arriving updates.
• Implement repeatable writes and reconciliation checks so rerunning failed loads does not create inconsistent results.
• Validate source-to-target counts, time ranges and completeness while preserving identifiers, units and quality information.
• Diagnose failures involving connectivity, permissions, source changes and malformed records.

Data modelling and transformation

• Implement approved spaces, containers, views, data models, instances and relationships.
• Reuse or extend Core Data Model concepts in line with the solution design.
• Develop SQL transformations and Python processing for cleansing, normalization, mapping and enrichment.
• Implement stable identifiers and source crosswalks, including collision checks for repeated tags across sites.