Data Platform Engineer

Job summary

Build and operate an enterprise data platform that integrates information across business systems into a trusted foundation for analytics, applications, and AI. The role includes developing production ETL/ELT pipelines, near-real-time integrations, reusable datasets, self-service reporting, and governance, using technologies such as Microsoft Fabric, Databricks, Snowflake, Python/PySpark, SQL, and Azure.

Qualifications

• Significant experience building and operating a modern data platform using Microsoft Fabric, Databricks, Snowflake, or a similar platform.
• Experience with medallion architecture, ingestion performance, and platform cost forecasting.
• Strong Python/PySpark and SQL skills, with experience building production ETL/ELT pipelines from varied business systems.
• Experience delivering self-service data and reporting with practical governance, including access control, classification, lineage, and data quality.
• Hands-on experience with an enterprise data catalogue for cross-system governance, ownership, and access management.
• Experience working with unstructured data and building knowledge graphs or ontologies.
• Experience building secure, scalable solutions on Azure or an equivalent cloud platform.
• Regular use of AI-assisted development tools and ability to review and validate generated output.
• Knowledge of secure, privacy-conscious AI solution design, testing, and cost control.
• Proven end-to-end delivery from concept to production in a small team, using CI/CD, DevOps, and iterative agile practices.

Responsibilities

• Build and operate a scalable enterprise data platform for analytics, integration, and AI.
• Integrate business and external data using APIs, change data capture, event streaming, and mirroring.
• Create a trusted knowledge layer connecting data with business definitions and context.
• Develop ETL/ELT pipelines and tested, reusable datasets; work with business owners to define and certify data and KPIs.
• Deliver self-service reporting and make data available to applications and AI tools.
• Implement access control, sensitive data classification, lineage, data quality, and observability.
• Monitor and manage platform performance and costs.
• Use AI-assisted development tools while maintaining engineering quality.
• Collaborate across business and technology teams and promote modern data and AI practices.

Skills

Industry

Relocation