• Design, implement, and maintain robust and scalable data pipelines using AWS, Azure, and containerization technologies. • Develop and maintain ETL/ELT processes to extract, transform, and load data from various sources into data warehouses and data lakes. • Collaborate with data scientists, analysts, and other engineers to ensure seamless data flow and availability across the organization. • Optimize data storage and retrieval performance by utilizing cloud services like AWS Redshift, Azure Synapse, or other relevant technologies. • Work with containerization tools like Docker and Kubernetes to ensure smooth deployment, scalability, and management of data pipelines. • Monitor, troubleshoot, and optimize data processing pipelines for performance, reliability, and cost-efficiency. • Automate manual data processing tasks and improve data quality by implementing data validation and monitoring systems. • Implement and maintain CI/CD pipelines for data workflow automation and deployment. • Ensure compliance with data governance, security, and privacy regulations across all data systems. • Participate in code reviews and ensure the use of best practices and documentation for data engineering solutions. • Stay up-to-date with the latest data engineering trends, cloud services, and technologies to continuously improve system performance and capabilities.

• Architect our data ecosystem, ensuring the platform powers frontend applications efficiently • Lead and mentor the team, driving technical excellence through code reviews and best practices in data modeling • Build and maintain automated DAGs and ELT workflows for reliable, 24/7 data availability • Design and optimize Snowflake datasets to support analytics and customer-facing features • Partner with DevOps to configure and manage AWS services for smooth data ingestion and transformation • Proactively monitor workflows, troubleshoot failures, and ensure reliability via dbt testing • Collaborate with developers to align Node.js backends and React frontends with the data architecture • Establish data standards and document pipelines, DAGs, and data models for onboarding and knowledge sharing

• Provide top-quality solution design and implementation for clients. • Provide support in defining the scope and the estimating of proposed solutions. • Engage with our clients to understand their strategic objectives. • Responsible for translating business requirements into technology solutions. • Work with the client and engagement leaders to define a project plan to meet delivery expectations. • Utilize Big Data technologies to architect and build a scalable data solution. • Identify any gaps and work with the client to resolve in a timely manner. • Stay current with technology trends in order to make useful recommendations to match the client requirements. • Responsible for the design and execution of abstractions and integration patterns (APIs) to solve complex distributed computing problem. • Participate in pre-sales activities, including proposal development, RFI/RFP response, shaping a solution from the client’s business problem. • Mentor and coach a team of data engineers. • Act as a thought leader in the industry by creating written collateral (white papers or POVs), participate in speaking events, create/participate in internal and external events (lunch ‘n learns, online speaking events, etc.)

• Manage and Develop a High-Performing Team: Hire, mentor, and manage a team of Data Engineers. Provide clear goals, career development guidance, and ongoing performance feedback. Foster accountability, ownership, and engineering excellence. • Own Data Engineering Execution: Oversee the design, development, and operation of scalable data pipelines and platform capabilities across our cloud data environment. Ensure reliable ingestion, transformation, and availability of structured and unstructured data. • Enforce Platform Best Practices: Implement and enforce industry best practices for data modeling, pipeline orchestration, testing, monitoring, observability, and cost efficiency across Azure-based data infrastructure (ADLS, Databricks, and related tooling). • Operational Excellence & Reliability: Define and manage SLAs for production data processes. Ensure high standards for data quality, reliability, and performance. Proactively identify risks and engineering bottlenecks before they impact the business. • Collaborate Cross-Functionally: Partner with Analytics Engineering and business stakeholders (Finance, Operations, Product, Revenue) to translate business requirements into scalable technical solutions. Ensure alignment between engineering execution and business priorities. • Support Governance & Compliance: Operationalize data governance, data protection, and compliance frameworks (including GDPR and other global requirements) in partnership with leadership. Ensure secure and responsible data management practices. • Enable BI and AI/ML Capabilities: Ensure the data platform effectively supports analytics, reporting, machine learning, and AI workloads through well-structured, discoverable, and trusted datasets. • Elevate Organizational Maturity: Improve engineering processes, documentation standards, code review rigor, deployment practices, and cross-team coordination to elevate the overall maturity of the Data Engineering function. • Partner in Planning & Roadmapping: Collaborate with the Director of Data Engineering on roadmap planning, resource allocation, and prioritization to ensure successful execution of strategic initiatives. • This role requires working overlapping hours with U.S.-based colleagues, including evening or overnight hours in India aligned to Central Standard Time (CST). • Additional duties and responsibilities as necessary.