Senior Technical Lead

Job Summary

Role Databricks Data Engineer / Data Product Engineer Contract Duration • Initial engagement: 3 months Extension based on Phase 1 outcomes Experience Mandatory 4-7 years Data Engineering experience 2+ years Databricks experience • strong SQL PySpark Data modeling experience • API integration & File Ingestion experience ETL/ELT development Experience working with business stakeholders Nice to Have • Google Analytics • Salesforce • SharePoint integration • Power Bl AI/ML exposure Key Deliverables Technical Deliverables • Source-to-target mapping Data ingestion pipelines Harmonization framework Reusable transformation logic • Gold-layer data products Business Deliverables • Customer/Consumer model • Sales analytics model Product analytics model Market intelligence model Operational Deliverables Data quality monitoring Pipeline monitoring • Technical documentation • Knowledge transfer Phase 1 Scope (3 Months) Month I: Foundation & Onboarding Understand global Databricks standards Review available data sources Design source-to-target mapping Build ingestion framework • Connect 2-3 priority data sources Month 2: Data Harmonization Build common business entities: Customer • Product • Country Dealer • Channel Implement: • Bronze layer • Silver layer • Data quality checks Month 3: First Reusable Data Product Deliver one high-value data product. Examples: Sales Performance Output should be consumable by: Power Bl Analytics Future Al use case

Key Responsibilities

Role Databricks Data Engineer / Data Product Engineer Contract Duration • Initial engagement: 3 months Extension based on Phase 1 outcomes Experience Mandatory 4-7 years Data Engineering experience 2+ years Databricks experience • strong SQL PySpark Data modeling experience • API integration & File Ingestion experience ETL/ELT development Experience working with business stakeholders Nice to Have • Google Analytics • Salesforce • SharePoint integration • Power Bl AI/ML exposure Key Deliverables Technical Deliverables • Source-to-target mapping Data ingestion pipelines Harmonization framework Reusable transformation logic • Gold-layer data products Business Deliverables • Customer/Consumer model • Sales analytics model Product analytics model Market intelligence model Operational Deliverables Data quality monitoring Pipeline monitoring • Technical documentation • Knowledge transfer Phase 1 Scope (3 Months) Month I: Foundation & Onboarding Understand global Databricks standards Review available data sources Design source-to-target mapping Build ingestion framework • Connect 2-3 priority data sources Month 2: Data Harmonization Build common business entities: Customer • Product • Country Dealer • Channel Implement: • Bronze layer • Silver layer • Data quality checks Month 3: First Reusable Data Product Deliver one high-value data product. Examples: Sales Performance Output should be consumable by: Power Bl Analytics Future Al use case

Skill Requirements

Role Databricks Data Engineer / Data Product Engineer Contract Duration • Initial engagement: 3 months Extension based on Phase 1 outcomes Experience Mandatory 4-7 years Data Engineering experience 2+ years Databricks experience • strong SQL PySpark Data modeling experience • API integration & File Ingestion experience ETL/ELT development Experience working with business stakeholders Nice to Have • Google Analytics • Salesforce • SharePoint integration • Power Bl AI/ML exposure Key Deliverables Technical Deliverables • Source-to-target mapping Data ingestion pipelines Harmonization framework Reusable transformation logic • Gold-layer data products Business Deliverables • Customer/Consumer model • Sales analytics model Product analytics model Market intelligence model Operational Deliverables Data quality monitoring Pipeline monitoring • Technical documentation • Knowledge transfer Phase 1 Scope (3 Months) Month I: Foundation & Onboarding Understand global Databricks standards Review available data sources Design source-to-target mapping Build ingestion framework • Connect 2-3 priority data sources Month 2: Data Harmonization Build common business entities: Customer • Product • Country Dealer • Channel Implement: • Bronze layer • Silver layer • Data quality checks Month 3: First Reusable Data Product Deliver one high-value data product. Examples: Sales Performance Output should be consumable by: Power Bl Analytics Future Al use case

Other Requirements