The Data Engineer will be central to building and evolving the data foundation that powers decision-making across the organization. You'll take full ownership of end-to-end data pipelines—from ingestion through transformation and analytics delivery—ensuring data is accurate, accessible, and actionable. This role operates at the intersection of engineering and analytics, requiring a strong technical foundation and a proactive mindset to support a fast-moving, AI-driven environment.
Key Responsibilities
- Design, implement, and maintain data pipelines using Fivetran and custom solutions to move data from GCP systems into Snowflake
- Lead the dbt modeling layer, structuring data for energy pricing, customer behavior, marketplace outcomes, and utility rates
- Develop clean, well-documented data models that serve business and operational teams
- Collaborate with engineering, operations, and leadership to deliver analytics that guide strategic decisions
- Build and maintain interactive dashboards in Hex to deliver insights to non-technical audiences
- Establish data contracts and schema standards to ensure consistency and reliability as the data ecosystem grows
- Integrate AI tools to automate data quality checks, detect anomalies in regulatory feeds, and enhance development speed with AI-assisted SQL and Python
Qualifications
Applicants should have 3–6+ years in data or analytics engineering, preferably in a high-growth or data-intensive environment such as energy, fintech, or marketplaces. Strong experience with dbt is essential, with an emphasis on writing maintainable, tested, and well-documented models. Proficiency in SQL is required, along with a mindset that prioritizes testing, documentation, and the needs of downstream users.
Preferred skills include Python for pipeline and tooling development, hands-on experience with Snowflake schema design and performance optimization, and familiarity with GCP services like BigQuery, Cloud Storage, and Pub/Sub. Experience with Hex, Looker, or similar dashboarding platforms is a plus. Candidates should also demonstrate curiosity about energy markets and a personal practice of using AI to improve productivity.
Environment & Impact
This role is part of a lean, high-ownership team where initiative directly shapes outcomes. The environment is fast-paced, AI-first, and focused on building intelligent data systems. While the position offers location flexibility, there is a strong emphasis on in-person collaboration to maintain alignment and momentum. You’ll play a key role in defining how AI is integrated into data workflows and in scaling a trusted, high-performance data platform.
