Senior Technical Lead

Job description

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 Apply now Information at a Glance Why HCLTech? At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first. HCLTech is a global technology company, home to more than 230,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending September 2026 totaled $14.8 billion. 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