Data Engineering Manager (Databricks)
RemoteChile
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. Profile source data quality, ownership, and reconciliation requirements across enterprise systems while implementing integration pipelines that prepare data for AI-generated narratives. Collaborate with business owners and AI Engineers to define, validate, and translate KPI requirements into reusable data structures, enforcing security, access control, and governance practices aligned with platform standards. Lead technical documentation, production readiness assessments, and deployment oversight. Requires 7+ years in Data Engineering with expertise in semantic modeling, SQL, Python, and Azure Databricks. English advanced proficiency required for global team communication.
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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