• 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

• Own and evolve our data platform, tooling, and architecture to meet growing analytical and product needs. • Help lead the delivery of a v2 data architecture at Postscript via a data lakehouse • Build stream and batch data ingestion pipelines to process source data • Partner with Engineering, Product, and BI to support net new analytics use cases and migrations • Support existing data engineering systems • Mentor and coach other data engineers and backend software engineers in the data practice

• Serás una pieza clave en el equipo de Data Integration, creando e implementando arquitecturas modernas de datos sobre Google Cloud Platform (GCP). • Diseñarás, mantendrás y optimizarás sistemas de procesamiento paralelo. • Participarás activamente en la transformación y modelado de datos utilizando dbt. • Desarrollar pipelines en Python (PySpark) y Apache Beam, procesando datos estructurados y semiestructurados. • Analizar y validar la calidad de los datos, asegurando consistencia y precisión. • Colaborar con equipos internos y clientes, aportando una visión consultiva que genere valor en cada etapa del proyecto.