Data Engineer

The Data Engineer converts ingested data into trusted, tested, documented and performant analytical data products. The role builds the Bronze, Silver and Gold layers of the Lakehouse and the reusable domain models that dashboards, Genie Spaces, forecasts, machine learning and AI applications all depend on.

Data product engineering

• Build and maintain Bronze to Silver to Gold transformations and automated data-cleaning pipelines.
• Create aggregated tables that shorten the path from raw data to dashboards and Genie insights.
• Design reusable customer, product, location, asset, material, transaction and process models.

Quality and correctness

• Implement business rules, historical and slowly changing logic, reconciliations and data-quality controls.
• Build data-quality monitoring and publish quality metrics for certified datasets.
• Investigate and resolve data defects raised by consumers.

Performance and operations

• Optimize storage, partitioning, compute, orchestration and cost.
• Implement testing, version control and CI/CD for pipelines.
• Support production pipelines, including on-call or scheduled support arrangements.

Enablement

• Support feature pipelines for machine learning, forecast inputs, semantic layers, Genie Spaces and AI applications.
• Document lineage, definitions and refresh behaviour for every certified dataset.

• Certified datasets and reusable domain models.
• Tested, version-controlled and documented pipelines.
• Data-quality dashboards and control reports.
• Lineage and technical documentation.
• Performance and cost-optimization improvements.

• Correctness of data against source and business rules.
• Freshness and reliability of certified data products.
• Performance, cost efficiency and maintainability of pipelines.
• Completeness of lineage and documentation.
• Reproducibility — a result produced today can be reproduced tomorrow.

• Internal: Systems Integration Engineers; BI & AI Context Engineers; Data Scientists; AI / LLM Engineers; Data Governance Specialist; Data & AI Translators; Data Engineering Capability Head.

External: platform vendor support.

Education

Bachelor's degree in Computer Science, Information Technology, Engineering, Statistics or a related field.

Experience

Three or more years building production data pipelines. Demonstrated delivery of dimensional or domain models serving analytics and machine learning workloads. Databricks or equivalent Lakehouse experience strongly preferred.

Certifications

Preferred: Databricks Certified Data Engineer Associate or Professional. Optional: cloud platform data engineering certification.

• Advanced SQL and Python; PySpark.
• Databricks, Delta Lake, Unity Catalog and medallion architecture.
• Dimensional and domain data modelling.
• Orchestration and workflow scheduling.
• Data-quality frameworks and testing.
• Git, CI/CD and code review practice.
• Performance tuning and cost management.

• Rigour — will not ship a number that has not been reconciled.
• Systems thinking about downstream consumers.
• Ownership of production outcomes, not just code delivery.
• Constructive code review and knowledge sharing.
• Continuous improvement and automation instinct.

Levels below are indicative and subject to OD and HR job evaluation.

Level

Expectation

Data Engineer

Builds and maintains assigned pipelines and models under technical direction. Three or more years of relevant experience.

Senior Data Engineer

Owns a domain's data products end to end, sets patterns, reviews others' work. Six or more years of relevant experience.

Lead Data Engineer

Sets Lakehouse architecture and engineering standards across domains and deputizes for the Capability Head. Nine or more years of relevant experience.

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