Lead Data Engineer

1. Plan, Design, and Execute

• Partner with data product managers to deliver annual data roadmaps. 
• Accountable for ETL designs across data sources. 
• Execute ETLs across data sources. 
• Develop integration approach for data products to support AI and advanced analytics. 
• Establish best practices for medallion architecture (bronze/silver/gold layers), data lineage, and performance optimization. 

2. Data Extraction, Load, and Transformation

• Design, build and oversee pipelines ingesting all internal (SAP S/4HANA, legacy ERP, CRM, manufacturing, supply chain, HR) and external data sources (market data, retail scanner data, syndicated data, partner APIs, IoT feeds). 
• Oversee the design of complex pipelines, setting the direction and standards. 
• Ensure all data products feed off of lakehouse data that has SAP-embedded data characteristics. 
• Standardize reusable ingestion frameworks, metadata-driven pipelines, and CI/CD-enabled deployment patterns. 
• Evaluate & define suitable tools (e.g., Kafka, Sqoop, Fivetran, custom Python/Scala scripts) for optimal extraction. 

3. Data quality and governance

• Establish validation checks during extraction and load (schema enforcement, deduplication, anomaly detection) and ensure data classification alignment, enforcement & audit traceability. 
• Implement monitoring and alerting systems to catch pipeline failures early. 
• Enforce regulatory compliance (e.g., GDPR, PDPA). 
• Manage PII across systems and workflows  

4. Driving innovation and strategy

• Drive automation in pipeline deployment (CI/CD for data workflows). 
• Review and recommend emerging tools (dbt, Airbyte, Delta Lake) to modernize extraction and load processes. 
• Champion cloud-native solutions for scalability and cost efficiency.  

5. Issue Resolution and Support

• Monitor and troubleshoot the data pipelines proactively across data sources. 
• Provide expert-level support and guidance to data teams across the Enterprise.