• Design, build, and optimize ETL/ELT workflows using tools like Databricks, SQL, Python/pyspark & Alteryx (Good to have) • Develop and maintain robust, scalable, and efficient data pipelines for processing large datasets • Work on cloud platforms (Azure, AWS) to build and manage data lakes, data warehouses, and scalable data architectures • Utilize cloud services like Azure Data Factory, AWS Glue, or similar for data processing and orchestration • Use Databricks for big data processing, analytics, and real-time data processing • Create and manage SQL-based data solutions, ensuring high availability, scalability, and performance • Collaborate with cross-functional teams to deliver impactful data solutions • Leverage CI/CD pipelines to streamline development, testing, and deployment of data engineering workflows • Maintain clear documentation for data workflows, pipelines, and processes

• Design and implement enterprise-scale data pipelines using Databricks on AWS, leveraging both cluster-based and serverless compute paradigms • Architect and maintain medallion architecture (Bronze/Silver/Gold) data lakes and lakehouses • Develop and optimize Delta Lake tables for ACID transactions and efficient data management • Build and maintain real-time and batch data processing workflows • Create reusable, modular data transformation logic using DBT to ensure data quality and consistency across the organization • Develop complex Python applications for data ingestion, transformation, and orchestration • Write optimized SQL queries and implement performance tuning strategies for large-scale datasets • Implement comprehensive data quality checks, testing frameworks, and monitoring solutions • Design and implement CI/CD pipelines for automated testing, deployment, and rollback of data artifacts • Configure and optimize Databricks clusters, job scheduling, and workspace management • Implement version control best practices using Git and collaborative development workflows • Partner with data analysts, data scientists, and business stakeholders to understand requirements and deliver solutions • Mentor junior engineers and promote best practices in data engineering • Document technical designs, data lineage, and operational procedures • Participate in code reviews and contribute to team knowledge sharing

• Architect complex, high-volume data pipelines for production use. • Design and implement scalable data models serving multiple product and internal teams. • Own data quality frameworks and standards across key data products. • Build reusable patterns for transformations and metrics to drive efficiency. • Define and maintain core business metrics and Key Performance Indicators (KPIs) in partnership with Analytics. • Own the data products used across the company, ensuring reliability and performance. • Set and promote standards for data modeling and pipeline development. • Partner closely with Analytics, Data Science, and Machine Learning teams on requirements to reduce friction and accelerate their work. • Mentor engineers and actively participate in the hiring process.

• Design and evolve scalable, distributed data infrastructure across cloud platforms • Build and maintain real time and batch data processing pipelines supporting analytics and AI/ML workloads • Develop and manage integrations with third party e-commerce platforms to expand our data ecosystem • Ensure data availability, reliability and quality through monitoring and automated auditing • Partner with engineering, AI and product teams on data solutions for business critical needs • Mentor and support data engineers, establishing best practices and code quality standards