• 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

• Lead a team of data engineers providing direction, training, and guidance to implement software solutions • Collaborate with a cross functional team to implement solutions tightly aligned with business objectives • Partner with other engineering leads to design cloud infrastructure solutions • Use the latest data flow technologies and cloud services to produce exceptional software within a continuous integration/continuous delivery environment • Mentor team members and help plan and prioritize engineering team activities • Ensure standards and framework compliance across the development team • Proactively research industry trends and best practices to apply them as necessary • Participate in Agile activities including daily stand ups, estimations, and backlog grooming and reviews