Data Engineering Manager (Databricks)

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: 🏥 Health and Well-being: - At-home medical assistance via EMI (or similar provider) through Asobursatil, available for all employees from AllStar to Analyst level. - Private healthcare plans for Lead-level roles and above. 🎉 Celebrations and Recognitions: - Christmas kit delivered to all employees. - 1 day off for academic graduation. - Family Day: 1 day off every semester (must be taken within the same semester). 💰 Financial Health and Savings (Work Together, Get Together Program): - Savings incentive program via Asobursatil: - Year 1: Blend contributes 50% of your monthly savings. - Year 2: Blend contributes 100% of your monthly savings. - Year 3+: Blend contributes 150% of your monthly savings. - Savings can be withdrawn in July and December. 📚 Educational Loans and Subsidies: - Forgivable education loans subject to committee approval and budget availability. - Requirements: 1+ year at Blend, no disciplinary actions in the past 6 months, successful completion of prior training, and knowledge sharing within 6 months post-training. - Retention