Data Engineering Manager (Databricks)

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com We are seeking a Data Engineering Manager to contribute to our next level of growth and expansion.

What is this position about? Design, build, and maintain semantic layers and KPI models using Databricks Metric Views to underpin governed executive scorecards and AI-powered analytical solutions. Work directly with business owners to define, validate, and translate KPI requirements into reusable data models and business logic. Profile source data quality, ownership, data grain, and reconciliation requirements across enterprise source systems. Design and implement data integration and transformation pipelines that prepare enterprise data for AI-generated narratives and conversational analytics. Define conformed dimensions and market-specific data variations to support multi-market reporting and analytics. Collaborate closely with Business Analysts and AI Engineers to align KPI definitions with underlying data structures and business ontologies. Establish and enforce data quality, validation, and monitoring frameworks across all data assets feeding analytical applications. Implement security, access control, and governance practices aligned with platform and AI governance standards. Lead technical documentation and knowledge transfer initiatives at the conclusion of each delivery phase. Support production readiness assessments and oversee the deployment of solutions to production environments.

7+ years of experience in Data Engineering, with demonstrated expertise in semantic layer and KPI/metric modeling. Strong hands-on experience building and maintaining Databricks Metric Views or equivalent semantic/metric layer tooling. Advanced proficiency in SQL and Python for data processing, transformation, and pipeline development. Solid understanding of cloud data platforms, specifically Azure Databricks, and modern ELT/ETL tooling. Demonstrated expertise in data modeling techniques, conformed dimensions, and Medallion-style architectures. Experience profiling data quality, lineage, and reconciliation across multiple source systems. Comfort working directly with business stakeholders to gather, validate, and implement KPI requirements. Understanding of business ontology and semantic modeling concepts. Proficiency with Git version control and collaborative development practices. Knowledge of how data engineering supports AI/LLM-based analytics, including feature preparation for narrative generation and conversational analytics. Experience with FMCG/CPG or retail data ecosystems (POS, SKU, category, and market performance datasets) is a plus. What about languages? English: Advanced (required for effective communication with global teams) How much experience must I have? 7+ years of experience in Data Engineering or related disciplines such as Data Architecture or Analytics Engineering, with demonstrated expertise in semantic modeling, KPI development, and multi-source data integration.

Our Perks and Benefits: 📚 Learning Opportunities: Certifications in AWS (we are AWS Partners), Databricks, and Snowflake. Access to AI learning paths to stay up to date with the latest technologies. Study plans, courses, and additional certifications tailored to your role. Access to Udemy