Continental - Technical Lead - Data Lakehouse

About the job :

Job Description :

- 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.

- Provide expert consulting, quality assurance, and enablement for data engineering and data science teams.

- Act as a design authority for data warehouse, semantic modeling, and advanced analytics integration.

- 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.

- Collaborates closely with platform architects, data scientists, governance teams, and functional IT globally.

Main Tasks :

- Define enterprise 3NF and warehouse modeling standards.

- Maintain and review enterprise-wide data & analytics models and shared artifacts.

- Align naming conventions and metadata handling with governance standards.

- Guide partitioning, indexing, and performance tuning.

- Enable, steer and optimize semantic integration with Power BI, live tabular exploration and other tools.

- Own common functions, i.e. FX conversion, BOM logic, time-slicing.

- Review and approve core components for quality and reusability.

- Provide support on high-performance or high-complexity challenges.

- Align lakehouse implementation with architectural decisions.

- Collaborate with data science and AI teams on model deployment.

- Ensure seamless integration of ML/AI pipelines into the lakehouse.

- Support LLM and external API integration patterns.

- Build and maintain shared libraries and data engineering templates.

- Coach junior engineers and define TDD and "as-code" standards.

- Drive engineering excellence across the community of practice.

- Maintain architectural blueprints, templates, and best practices.

- Publish design guidelines and coding standards.

- Create re-usable architecture patterns for lakehouse environments.

- Monitor usage and implement auto-scaling policies.

- Analyze and optimize cluster configurations for cost-efficiency.

- Provide cost transparency and usage reporting to stakeholders.

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