Posted:12 hours ago|
Platform:
Work from Office
Full Time
* Govern the enterprise-wide standards for data & analytics modeling and performance within the Databricks Lakehouse.
* Drive consistency and reuse of core data & analytics artifacts and ensure scalable integration across all business domains.
* Acts as the senior engineering point of contact for the lakehouse layer across global teams.
* Coordinates with 25+ data engineering and data science professionals across domains and geographies.
Main Tasks:
Define enterprise 3NF and warehouse modeling standards.
Maintain and review enterprise-wide data & analytics models and shared artifacts.
Guide partitioning, indexing, and performance tuning.
Enable, steer and optimize semantic integration with Power BI, live tabular exploration and other tools.
Review and approve core components for quality and reusability.
Provide support on high-performance or high-complexity challenges.
Collaborate with data science and AI teams on model deployment.
Ensure seamless integration of ML/AI pipelines into the lakehouse.
Build and maintain shared libraries and data engineering templates.
Coach junior engineers and define TDD and "as-code" standards.
Maintain architectural blueprints, templates, and best practices.
Publish design guidelines and coding standards.
Monitor usage and implement auto-scaling policies.
Analyze and optimize cluster configurations for cost-efficiency.
Qualifications
Degree in Computer Science or related field; certifications in Databricks or Microsoft preferred.
6-10 years in data engineering with focus on enterprise data & analytics warehouse, lakehouse modeling, and ML integration.
Hands-on experience designing large-scale semantic, warehouse, and advanced analytics layers.
Track record of architectural ownership and peer enablement with diverse teams.
Experience working in international teams across multiple time zones and cultures, preferably with teams in India, Germany, and the Philippines.
Continental
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