Data Engineering Manager, Databricks
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Job Description
Data Engineering Manager building Databricks semantic layers and KPI models for Blend, an AI services provider. Enabling governed scorecards, AI narratives, and conversational analytics.
Responsibilities:
- Design, build, and maintain semantic layers and KPI models using Databricks Metric Views
- Underpin governed executive scorecards and AI-powered analytical solutions
- Work 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 for AI-generated narratives and conversational analytics
- Define conformed dimensions and market-specific data variations for multi-market reporting and analytics
- Collaborate with Business Analysts and AI Engineers to align KPI definitions with data structures and business ontologies
- Establish and enforce data quality, validation, and monitoring frameworks across data assets
- 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 deployment to production environments
Requirements:
- 7+ years of experience in Data Engineering or related disciplines such as Data Architecture or Analytics Engineering
- 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
- English: Advanced (required for effective communication with global teams)
Benefits:
- 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 Business, offering thousands of courses to boost your technical and soft skills
- English lessons to support your professional communication
- Travel opportunities to attend industry conferences and meet clients
- Career development plans and mentorship programs
- Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones
- Company-provided equipment
- Flexible working options
- Other benefits may vary according to your location in LATAM
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