Data Engineer

1) Lead the design, governance, and continuous evolution of enterprise data models and data products, including conceptual, logical, and physical data modelling. Review and approve data model changes, new source integrations, tables, attributes, and relationships introduced across data products to ensure alignment with enterprise data architecture standards, modelling principles, scalability, reusability, and business requirements.

2) Design and build reliable, high-performance data engineering solutions including data ingestion, transformation, integration, and orchestration pipelines across cloud data platforms, ensuring data quality, security, governance, scalability, and operational excellence throughout the enterprise data lifecycle across Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains.

3) Partner with Data Engineers, Solution Engineers, Analysts, and Business Stakeholders to translate business requirements into governed, discoverable, and AI-ready data products by applying Data-as-a-Product principles, metadata standards, lineage, semantic modelling, and data governance practices that enable trusted enterprise data

Required Skills, Education, or Experience

1) 8+ years of strong experience in enterprise data modelling and data engineering, supporting large-scale data warehouses, lakehouses, semantic models, and enterprise data products on modern cloud platforms.

2) Strong expertise in enterprise data modelling, including conceptual, logical, and physical data models, dimensional modelling (Star/Snowflake), Data Vault, normalized and canonical models, semantic modelling, master data management, and enterprise data architecture standards.

3) Proven experience governing and reviewing enterprise data models across multiple data products, including evaluation of new source systems, entities, attributes, relationships, and model enhancements to ensure alignment with enterprise architecture principles, data standards, scalability, reusability, and long-term maintainability.

4) Experience designing and implementing data solutions across the complete data lifecycle, including Source Systems, Data Ingestion, Raw/Bronze, Curated/Silver, Business/Gold, Semantic Layers, and Enterprise Data Products, ensuring data quality, governance, security, performance, and operational excellence.

5) Strong hands-on expertise in data engineering and cloud data platforms, including Snowflake, Databricks, SQL, Matillion, dbt,Power BI with experience building scalable, high-performance data pipelines, data integration solutions, and reusable enterprise data assets.

6) Proven ability to lead technical design and data model reviews and collaborate with Solution Engineers, Data Engineers, Architects, and Business Stakeholders to translate business requirements into scalable data products and establish enterprise-wide data modelling, engineering, and governance best practices.