BC Sector O and G-Cognite-Manager

EY; Pune District; Hybrid; INR 3,000,000 - 6,000,000; Full time

Benefits offered by this job

Global EY network

Hands-on leadership role

Learning opportunities

Collaborative environment

Job summary

EY GDS | Cognite Data Engineering Lead to manage design and delivery of data pipelines and contextualization for Oil & Gas using Cognite Data Fusion. You’ll own implementation quality, guide architecture decisions and deliver usable data for operational and analytical solutions.

Ideal candidates have 10+ years in data engineering, strong Python/SQL, Cognite skills, and a track record of building reliable pipelines with CI/CD.

Qualifications

• A relevant engineering, computer science or information systems degree, or equivalent technical experience.
• Substantial hands-on Cognite Data Fusion implementation experience.
• Strong Python and SQL skills, including Cognite SDK and REST API usage.
• Experience implementing CDF data models, transformations and contextualization.
• Experience building pipelines with incremental loading, reconciliation, backfill and recovery.
• Practical knowledge of Git, automated testing, CI/CD and deployment configuration.
• Familiarity with authentication and the CDF capabilities required by integration services.
• Strong implementation knowledge in at least one upstream or downstream setting, with ability to apply relevant concepts to the other.
• Evidence of personally delivering and troubleshooting data pipelines used by operational or engineering applications.

Responsibilities

• Configure Cognite extractors and implement API or custom integrations as required.
• Build source-appropriate incremental loads with persistent checkpoints, retries and restart behaviour.
• Implement repeatable writes and reconciliation checks to avoid inconsistent results.
• Validate source-to-target counts, time ranges and completeness while preserving identifiers and quality information.
• Diagnose failures involving connectivity, permissions, source changes and malformed records.
• Implement approved data models, spaces, containers, views and transformations in Cognite.
• Develop SQL transformations and Python processing for cleansing and enrichment.
• Implement stable identifiers and crosswalks, including collision checks for repeated tags.
• Test migrations, deploy with Cognite Toolkit and monitor pipelines; maintain runbooks.

Skills

Python

SQL

Cognite Data Fusion (CDF)

CDF SDK & REST API

Data modeling

ETL pipelines

CI/CD

Git

Education

Engineering/CS degree or equivalent

Tools

Cognite Toolkit

Job description

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. 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.

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.
• Preserve distinctions between equipment, functional locations and changing assignments; test schema changes and execute migrations without breaking dependent consumers.

Contextualization and Oil & Gas data

• Implement identifier-based matching, rules and supported contextualization services to connect operational and engineering records.
• Link time series, maintenance activities and documents to the correct equipment or functional location.
• Validate matches against reference mappings, assess coverage and false matches, and route unresolved cases for domain review.
• Maintain mappings when tags, equipment assignments, hierarchies or document revisions change.
• Preserve upstream well and wellbore identities, provenance, tests and intervention history; preserve downstream equipment relationships, units, laboratory timing, and links between diagrams, documents and measurements.

Testing, deployment and application handoff

• Write and review production Python and SQL code with automated tests.
• Test completeness, freshness, uniqueness, relationship integrity, time-zone handling and recovery after interruption.
• Maintain configuration in Git and automate supported deployment using Cognite Toolkit.
• Monitor extraction, transformation and workflow execution with actionable logs and failure handling; configure least-privilege access.
• Deliver documented consumer views and example SDK/API queries, verify access and performance, maintain runbooks, and identify design changes requiring architecture review.

Must Have Qualifications & Experience

• A relevant engineering, computer science or information systems degree, or equivalent technical experience.
• Substantial hands-on Cognite Data Fusion implementation experience and understanding of production operation.
• Strong Python and SQL skills, including Cognite SDK and REST API usage.
• Experience implementing CDF data models, transformations and contextualization.
• Experience building pipelines with incremental loading, reconciliation, backfill and recovery.
• Practical knowledge of Git, automated testing, CI/CD and deployment configuration.
• Familiarity with authentication and the CDF capabilities required by integration services.
• Strong implementation knowledge in at least one upstream or downstream setting, with ability to apply relevant concepts to the other.
• Evidence of personally delivering and troubleshooting data pipelines used by operational or engineering applications.

Good to Have

• Experience with Cognite Toolkit, extraction pipeline monitoring and workflow orchestration.
• Familiarity with AVEVA PI, SAP maintenance data, IBM Maximo, laboratory systems or well-data services.
• Experience with WITSML, OPC UA, database extraction or enterprise APIs.
• Experience with document or diagram contextualization and 3D associations.
• Working knowledge of a major cloud platform and containerized integration services.
• Relevant Cognite Academy training or certifications.

What We Offer

• Opportunity to solve high-impact industrial data and analytics challenges across upstream and downstream Oil & Gas.
• A hands-on leadership role with scope to establish reusable practices and guide multidisciplinary teams.
• A collaborative environment spanning domain engineering, data, technology, product and global EY teams.
• Access to EY's global network, technology ecosystem and learning opportunities while contributing to a better working world.

What We Look For

A technical lead who can demonstrate a functioning ingestion-to-consumption workflow, explain their personal implementation contribution, and show how failed loads, duplicate records, incorrect relationships and application-access issues are detected and corrected.

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