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Strong brand, generalist Data Engineer title, and broad tooling/platform requirements drive high competition.
Core data engineering skills are transferable, but enterprise modelling and domain knowledge increase sensitivity to medium.
Explicit 8+ years plus mandated enterprise data-modelling and cloud tooling makes shortlisting highly strict.
Lead design, governance, and continuous evolution of enterprise data models and data products, ensuring alignment with architecture standards and business requirements.
Design and build scalable, high-performance data engineering solutions including data ingestion, transformation, and integration pipelines across cloud platforms ensuring data quality, security, and governance.
Collaborate with technical teams and business stakeholders to translate requirements into governed, discoverable, AI-ready data products applying Data-as-a-Product principles and metadata standards.
8+ years of experience in enterprise data modeling and data engineering supporting large-scale data warehouses and cloud platforms.
Bachelor's degree in Computer Science, IT, Data Analytics, Engineering, Information Systems, or related field; Master's degree preferred.
Strong hands-on expertise in data engineering and cloud data platforms such as Snowflake, Databricks, SQL, Matillion, dbt, Power BI.
Work Experience Required: 8+ years in relevant discipline as outlined
Experienced in enterprise data modeling techniques including dimensional modeling, Data Vault, normalized and canonical models, semantic modeling, and master data management.
Proven ability to govern and review complex data models ensuring scalability, reusability, and maintainability across multiple data products.
Familiar with Data-as-a-Product operating models, metadata management, data lineage, and advanced analytics platforms supporting AI/ML initiatives.