Data Engineering Manager (Databricks)
RemoteUnited States or Argentina
Job Summary
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, while profiling source data quality and ownership across enterprise systems. Implement data integration pipelines that prepare data for AI-generated narratives and establish security, access control, and governance practices aligned with platform standards. Lead technical documentation, support production readiness assessments, and oversee solution deployment. Requires 7+ years in data engineering with expertise in semantic modeling, SQL, Python, and Azure Databricks. Join Blend to co-create meaningful impact through data science and AI.
Required Qualifications
- 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
- English: Advanced (required for effective communication with global teams)
- 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
Desired Qualifications
- Experience with FMCG/CPG or retail data ecosystems (POS, SKU, category, and market performance datasets) is a plus
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