• Define and evolve data architecture standards for analytics and reporting, including data modeling, naming conventions, schema design, and documentation practices across the organization • Own the data catalog and metadata strategy, partnering with stakeholders to define, name, and organize data assets across multiple domains and source systems • Collaborate closely with Principal Data Engineering leadership and application engineering teams to align on ELT patterns, Snowflake usage, schema evolution, and analytical data modeling practices • Contribute hands‑on through SQL and Python, developing reference data models, prototypes, templates, and example implementations that demonstrate architectural intent • Support and enable data analysts by establishing consistent data usage, modeling standards, and shared definitions across a wide range of technical skill levels • Partner with application engineers on schema design to support rapid application development and reliable integration between operational and analytical data systems • Support PostgreSQL (including AWS Aurora) and Snowflake data modeling and analytical access patterns in collaboration with platform and database stakeholders • Establish and promote data governance practices covering data quality, ownership, lifecycle management, and schema change management • Drive incremental delivery of data architecture improvements, aligning short‑term progress with a clear long‑term architectural vision • Design high-ingestion pipelines (using tools like InfluxDB, Timescale, or Snowflake) capable of handling millions of data points per second from globally distributed battery sites • Ensure data can be seamlessly ingested from various industrial protocols such as Modbus, CAN bus, or DNP3, and translated into standardized cloud formats • Ensure data architectures comply with grid-specific regulations (like NERC CIP) and mandate on-site data storage for grid resilience • Help set the vision, roadmap and communicate the enterprise data strategy for the company

• Prepare technical architecture and design documents; • Manage the Databricks environment; • Manage cloud components on AWS, Azure, and GCP; • Develop solutions using Java, Python, Spark, and Scala; • Configure networking components in hybrid environments; • Define best practices for relational and non-relational data storage; • Implement improvements to Data Lake and Data Warehouse environments; • Implement and manage SQL and NoSQL databases; • Define and develop DevOps, Docker, and Kubernetes solutions; • Discuss and determine the best approaches for system integrations; • Support the creation of data governance processes; • Discuss the best solutions to support the company’s self-service BI strategy.

• Build shared platform services, tooling, and automation that support trusted data products across teams and domains • Develop reusable engineering patterns for ingestion, transformation, publishing, and operational workflows • Build and maintain platform components, automation, and engineering utilities using Python and other appropriate technologies • Contribute to internal tools and workflows that improve platform usability and reduce manual effort • Partner with senior engineers to implement scalable, maintainable platform solutions • Use infrastructure as code and CI/CD practices to improve consistency, repeatability, and maintainability • Support secure integration patterns for internally managed tools and connected SaaS solutions • Strengthen platform operability through observability, production-ready patterns, and practical automation • Collaborate with engineering and cross-functional stakeholders to turn platform needs into reliable technical solutions • Apply AI-assisted development practices where they improve engineering productivity and workflow efficiency • Contribute to strong engineering standards through thoughtful implementation, code quality, testing, and peer review

• Design and build shared platform capabilities that support trusted data products across teams and domains • Develop internal services, tooling, and automation that improve platform usability and reduce manual effort • Create reusable engineering patterns for ingestion, transformation, publishing, and operational workflows • Build and maintain platform components, automation, and engineering utilities using Python and other appropriate technologies • Contribute to the architecture and evolution of the core data platform and supporting engineering workflows • Use infrastructure as code and CI/CD practices to improve consistency, repeatability, and maintainability • Help define secure integration approaches for internally managed tools and connected SaaS solutions • Strengthen platform operability through observability, supportable production patterns, and practical automation • Partner with engineering and cross-functional stakeholders to translate platform needs into scalable technical solutions • Contribute to a more AI-enabled developer experience by applying AI-assisted development practices where they improve engineering productivity and workflow efficiency • Raise the technical bar through sound engineering judgment, thoughtful design, and strong coding and review practices