• Implementing source management through data generation and schema management • Define data ingestion pipelines that normalize and transform clients data to fit our standard data schema • Ensure data meet quality standards and routinely work with our customers, handling their requests and configuring the platform to their needs • The drive to collaborate, gather feedback, solve problems, and tackle challenges through test and learn is highly valuable in this position

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Lead the design and implementation of complex data ingestion pipelines using PySpark, Databricks Intelligence Platform, and AWS services (S3, IAM roles and policies), including Kafka/streaming architectures, schema evolution, and Delta Share, ensuring alignment with enterprise architecture standards. • Serve as a subject matter expert (SME) for data engineering, providing technical guidance and mentorship to engineers across the team and representing data engineering in cross-functional discussions. • Take ownership of platform reliability and performance, proactively identifying optimization opportunities across data ingestion, transformation, and storage layers. • Troubleshoot complex pipeline failures across multi-system dependencies. • Lead cross-functional technical initiatives, collaborating with data scientists, analysts, DevOps engineers, client teams (upstream data providers and downstream consumers), and stakeholders to deliver integrated solutions that meet program objectives. • Contribute to the data platform technical vision and architecture, making strategic decisions on tooling, frameworks, and design patterns that shape the program's long-term data engineering direction. • Define and enforce engineering standards, including code quality, testing practices, CI/CD processes, and infrastructure as code patterns using Terraform. • Evaluation of new technologies and approaches, presenting proof of concepts and technical roadmaps to leadership. • Author and maintain architectural documentation, technical decision records, and platform runbooks that enable team autonomy and operational excellence. • Other relevant duties as assigned and qualified/trained to perform.