• Design and build scalable, high-performance ETL and ELT pipelines that transform raw enterprise data into analytics-ready datasets.
• Lead the development of reliable batch and near real-time data processing frameworks supporting operational reporting and advanced analytics.
• Establish engineering patterns for reusable, modular data pipelines across multiple business domains.
• Ensure pipelines are production-grade with appropriate testing, observability, and operational monitoring

Data Product Engineering

• Design and deliver domain-level data products supporting retail, planning, merchandising, and supply chain analytics.
• Structure datasets to support diverse consumption patterns including BI reporting, advanced analytics, and data science workloads.
• Enable consistent, governed data access across enterprise analytics platforms.

Data Modeling and Analytics Enablement

• Design scalable data models such as star and snowflake schemas optimized for analytical workloads.
• Build analytics-ready datasets supporting key business metrics including sales, inventory performance, demand planning, and operational KPIs.
• Ensure data models are optimized for efficient integration with BI tools such as Power BI.

Performance, Scalability, and Platform Optimization

• Optimize data processing frameworks for performance, scalability, and cost efficiency across large datasets.
• Troubleshoot complex pipeline and infrastructure issues, identifying root causes and implementing sustainable solutions.
• Collaborate with platform engineering teams to improve cluster configuration, storage design, and processing efficiency.

Data Governance and Quality

• Implement robust data quality frameworks including validation, reconciliation, and monitoring of production datasets.
• Support enterprise governance initiatives including metadata management, lineage, and data catalog integration.
• Ensure data engineering solutions adhere to organizational security and compliance standards.

Collaboration and Technical Leadership

• Work closely with Data Product Managers, Data Analysts, and Data Scientists to translate business needs into scalable engineering solutions.
• Provide technical guidance and mentorship to junior data engineers.
• Contribute to engineering standards, architectural patterns, and platform best practices across the data organization.

Modern Data Platform Enablement

• Develop and deploy data solutions on modern cloud data platforms including Databricks and Microsoft Azure services such as Azure Data Factory and Synapse Analytics.
• Lead modernization initiatives transitioning legacy data pipelines to scalable cloud-native architectures.
• Contribute to CI/CD implementation and automation of data engineering workflows.