Principal Data Architect – Battery Storage

Role Overview

• 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

Requirements

• 8+ years of experience in data engineering, data architecture, or analytics platform development, with demonstrated ownership of cross‑team data standards and models
• Strong expertise in analytical data modeling, including dimensional modeling, semantic layers, and schema design for multi‑consumer analytics use cases
• Deep working knowledge of SQL and experience collaborating on or authoring complex analytical queries and models
• Proven experience designing or contributing to analytics platforms built on Snowflake or similar cloud data warehouses using ELT‑based architectures
• Experience defining and operationalizing data catalogs, metadata, and shared definitions, including naming conventions, ownership models, and documentation practices
• Experience designing or supporting data governance frameworks, including data quality, ownership, lifecycle management, and schema change management
• Experience supporting application teams on schema design to enable rapid application development and reliable data integration
• Familiarity with analytics engineering practices and tools (e.g., dbt or similar modeling frameworks)
• Proficiency with Python for data analysis, modeling, validation, or prototyping (not limited to production pipeline code)
• Experience partnering closely with data engineers on ELT patterns, schema evolution, and data quality practices
• Experience working with PostgreSQL or compatible systems (including managed services such as AWS Aurora) and understanding how operational schemas interact with analytical models
• Knowledge of AWS data services and cloud‑native data patterns strongly preferred
• Demonstrated ability to work effectively with data analysts across a wide range of technical skill levels, including analysts with limited engineering backgrounds
• Strong communication skills and a track record of cross‑functional collaboration with engineering, analytics, and business stakeholders
• Experience working in environments with large, diverse analyst populations and high data consumption across teams preferred
• Proven ability to deliver incremental architectural improvements while maintaining a clear long‑term vision for data consistency and scalability
• Background in energy, finance, trading, or other data‑intensive, operationally complex domains preferred.

Tech Stack

• AWS
• Cloud
• Postgres
• Python
• SQL

Benefits

• Highly competitive total compensation
• Flexible, work from home or hybrid work
• Unlimited vacation
• Work from home stipend
• Educational assistance
• Parental leave
• Highly engaging company culture with opportunities for in-person connection and learning and growth.