• Formulate, experiment with, and articulate alternative enterprise data architectures • Create comprehensive model, schema, ontologies, and interface specification documents, validating the data architecture against strict federal requirements and security assumptions • Design end-to-end data architectures, including data warehouses, data lakes, operational data stores, and metadata management repositories, aligning them with the Agile SAFe v6.0 roadmap • Author the blueprints for migrating and modernizing complex legacy data systems into highly distributed, cloud-native storage and analytical solutions specifically optimized for AWS GovCloud • Define enterprise data standards, data dictionaries, and master data management practices • Establish the governance models required to safely integrate third-party federal data sources while maintaining strict data lineage • Partner with backend engineers to design frameworks for high-volume, low-latency batch and stream processing pipelines (e.g., Kafka, Kinesis, Spark) that effortlessly bridge application and analytics tiers • Serve as the principal data advisor across the entire program • Work with software engineers, system architects, and customer application experts to evaluate technical trade-offs and drive advanced analytics capabilities • Leverage Jira, Confluence, and SAFe processes to translate high-level mission capabilities and agency mandates into foundational architectural epics, technical features, and data roadmaps.

• Design, develop, and optimize data infrastructure on Databricks • Architect pipelines using BigQuery, Google Cloud Storage, and Apache Airflow • Support the development and maintenance of data platform on GCP • Build, maintain, and improve ETL/ELT pipelines under guidance • Help implement and maintain data quality checks • Partner with Analytics, Product, and Data Science teams for reliable datasets for analytics and reporting

• Garantir a estabilidade, manutenção e evolução das soluções de dados que suportam projetos de IA • Analisar e corrigir falhas em pipelines de dados • Otimizar processos de ingestão e transformação de dados • Garantir qualidade e integridade das informações usadas em modelos de IA • Atuar em ambientes de alta complexidade, com arquiteturas distribuídas e governança de dados • Assegurar conformidade com normas como a LGPD • Orquestração: Airflow, Azure Data Factory • Processamento distribuído: Spark, Databricks • Bancos de dados: relacionais e NoSQL (MongoDB) • Linguagens: Python e SQL

Role Description Role: Data Engineer Location: Remote, USA Duration: Fulltime Employee - Manufacturing 8–10 years in manufacturing (optional) with hands-on experience in shop floor operations, production planning, and systems including MES, SCADA, and ERP. - Proficient in industrial protocols (OPC-UA, MQTT, Modbus) with ability to bridge OT/IT systems for real-time data extraction. - Applied experience with OEE, Six Sigma, SPC, and lean methodologies to drive measurable gains in yield, uptime, and efficiency. - Skilled in building scalable cloud data pipelines for high-volume manufacturing and IoT data using Spark, Kafka, Airflow, and Delta Lake. - Strong SQL and Python proficiency with hands-on experience in medallion/lakehouse architectures on Databricks, Snowflake, AWS, or Azure. Qualifications - Minimum 8 - 10 years of experience. - Strong in SQL and Python proficiency with hands-on experience in medallion/lakehouse architectures on Databricks, Snowflake, AWS, or Azure. Company Description We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work on a scale across all devices and digital mediums, and our people exist everywhere in the world (18000 plus experts across 35 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!