• Design, develop, and maintain data pipeline architectures. • Optimize data ingestion, storage, and processing workflows. • Collaborate with data scientists and analysts to understand data needs and convert requirements into technical specifications. • Ensure data quality and integrity throughout the data lifecycle. • Implement data security and compliance measures. • Monitor and troubleshoot data systems performance issues. • Stay up-to-date with industry trends, technologies, and best practices in data engineering.

• Enable efficient data access by creating and maintaining data pipelines. • Collaborate with ML engineers to design and maintain automation for machine learning training, quality assessment, and model release process. • Build data infrastructure from the vast amount of data for analytics, hypothesis testing and company metrics. • Identify, design and implement improvement to internal processes allowing to optimize data delivery, automate manual processes. • Design new and improve current patterns for building data models and implement necessary modifications.

• Develop and maintain ETL pipelines and data integration services using Python and SQL • Work with AWS services (S3, DynamoDB, Lambda, Glue) and NoSQL databases (MongoDB, DynamoDB) • Design, optimize, and validate data flows, ensuring data quality across systems • Collaborate with Senior engineers on architecture and performance improvements • Troubleshoot production data issues and perform root cause analyses • Contribute to the continuous improvement of development practices and performance monitoring

• Design, build, and maintain scalable data pipelines that ingest, clean, transform, and aggregate data from disparate sources across the VA ecosystem • Develop and implement data models that support analytical use cases and measurement frameworks • Partner with data scientists, service designers, and product managers to translate analytical requirements into infrastructure that delivers analytics-ready datasets • Implement data quality checks, validation processes, monitoring, and governance practices to ensure data accuracy and integrity • Communicate technical concepts and infrastructure decisions using plain language to non-technical stakeholders to support effective decision-making and gain buy-in where needed • Serve as a technical leader, mentor junior practitioners, and advance how data engineering supports human-centered design • Stay current on data engineering developments and bring best practices into constrained technical and policy environments