• Design, build, and maintain scalable ETL and ELT pipelines that transform raw enterprise and retail data into analytics-ready datasets.

• Support both batch and near real-time data processing to enable operational reporting, planning cycles, and analytical insights.

• Ensure pipelines are reliable, testable, and production-ready with appropriate validation and monitoring.

Data Modeling and Analytics Enablement

• Design and maintain data models such as star and snowflake schemas that support efficient querying and analytics consumption.

• Develop analytics-ready datasets aligned to retail KPIs including sales, inventory, demand, and supply chain performance.

• Optimize data structures to ensure seamless integration with BI tools such as Power BI and downstream analytics platforms.

Performance and Scalability Optimization

• Optimize data pipelines and storage layers for performance, scalability, and cost efficiency.

• Identify and resolve performance bottlenecks across data processing and consumption layers.

• Support platform-level optimization in collaboration with cloud and data platform teams.

Collaboration and Data Product Delivery

• Partner with Data Product Managers, Analysts, and Data Scientists to understand business requirements and deliver data products that serve diverse user needs.

• Work closely with Planning, Merchandising, and Supply Chain teams to translate functional requirements into technical solutions.

• Contribute to shared data standards, reusable patterns, and best practices across the analytics organization.

Data Governance and Quality

• Implement data quality, validation, and reconciliation checks to ensure trust and reliability of analytics data.

• Adhere to enterprise standards for data governance, security, and compliance.

• Support metadata, lineage, and documentation practices to improve transparency and maintainability.

Modern Data Platform Enablement

• Build and operate data solutions using modern cloud data platforms and services, including Databricks, Azure Data Factory, Synapse Analytics, and Azure SQL.

• Support migration and modernization initiatives as legacy pipelines are transitioned to cloud-native architectures.

• Contribute to CI/CD practices and environment automation for data pipelines.

Experience, Skills & Knowledge

• Proven experience in data engineering roles within a Retail, eCommerce, or Supply Chain domain.

• Hands-on experience with Databricks, Spark, and Prophecy.io or similar data orchestration tools.

• Experience migrating ETL pipelines from legacy platforms (Informatica, SSIS, Talend, Glue) to modern cloud environments.

• Strong proficiency in Python, PySpark, and SQL.

• Experience working with Retail ERP systems (SAP, D365, JDA) and logistics data feeds.

• Familiarity with data modeling for retail hierarchies and planning processes.

• Exposure to cloud platforms (AWS or Azure preferred) and cloud-native data services.

• Experience with CI/CD for data pipelines and environment automation.

• Strong analytical skills and ability to troubleshoot complex data issues.

• Excellent communication and collaboration skills.