Senior/Staff Backend Software Engineer - Data Platform The RoleWe're hiring a Senior/Staff Backend Software Engineer who has
built and run a data platform before - and wants to do it again for a problem that matters. You'll be a foundational owner of the platform behind Energize™: the shared ingestion, storage, compute, and serving layers that our modeling, product, and customer-facing teams all build on.
This isn't a role for someone who has only consumed a platform. We're looking for an engineer who has designed one: someone who has decided how data enters a system, defined the abstractions other engineers rely on, made the platform reliable enough that teams stop thinking about it, and lived with the consequences of those decisions in production. You'll bring that pattern recognition to GridCARE and set the technical direction for our platform on AWS.
You'll also build, monitor, and maintain teams of AI coding agents that accelerate how we develop and deliver software. Orchestration, observability, and scalability are central to the role.
Key Responsibilities- Owning the architecture and roadmap of GridCARE's data platform, treating it as an internal product with clear users, interfaces, and service levels.
- Designing how data enters the platform: ingestion architecture for heterogeneous energy and grid sources, the canonical data model, and the contracts that let sources change without breaking downstream consumers.
- Building the shared foundations other teams build on - orchestration, storage layers, compute frameworks, and reusable patterns - so modeling and product engineers can ship without reinventing infrastructure.
- Developing fast, reliable APIs and distributed processing services that connect energy data, analytical models, and customer workflows.
- Establishing platform standards for data quality, lineage, observability, access control, and cost management.
- Owning delivery from initial design through deployment and production operation, including on-call readiness and incident learning.
- Building and operating AI coding agent teams, including coordination and evaluation.
- Shaping technical decisions, mentoring engineers, and partnering with product and domain experts to understand what the platform needs to support next.
QualificationsRequired- Prior data platform experience: you've been a core builder or technical owner of a data platform used by multiple teams or products - not just pipelines for a single use case. Please be ready to walk us through a platform you built: what you designed, what you'd change, and how it evolved.
- Experience making foundational platform decisions - ingestion patterns, data modeling, schema evolution and data contracts, storage and compute layout - and carrying them through growth in data volume, sources, and users.
- Experience architecting and operating production backend systems, with clear judgment on design tradeoffs.
- Experience with distributed systems, concurrency, failure recovery, and safe migrations.
- Hands-on AWS experience, including deployment automation, monitoring, and secure access management.
- Strong Python, SQL, API design, and database modeling skills.
- Hands-on experience with modern backend and data infrastructure, such as Kubernetes, Docker, workflow orchestrators (Airflow, Prefect, or Dagster), dbt, PostgreSQL, cloud warehouses or lakehouses (Redshift, Athena, Snowflake, or Iceberg/Delta), and/or streaming or queuing systems (Kafka, Kinesis, or SQS).
- Experience developing ingestion and ETL/ELT pipelines into data marts and serving layers.
- Experience building and maintaining AI coding agent teams, including task delegation, context management, and evaluation.
- Strong testing, debugging, and technical communication skills.
Preferred- Early-stage startup experience with a demonstrable deliverable: you've worked at an early-stage startup (roughly seed through Series B) and can point to something concrete you shipped there - a platform, product, or system that reached production and that customers or internal teams relied on. Tell us what you built, what it enabled, and how you measured its impact.
- Experience taking a data platform from early stage (01) to scale, or re-architecting an existing platform while keeping it running.
- Experience designing self-serve platform capabilities: templates, SDKs, internal tooling, or developer-experience improvements that reduced the load on the platform team.
- Experience with data governance at the platform level: catalogs, lineage, data quality frameworks, and fine-grained access control.
- Full-stack development experience, including delivering features across backend services and frontend applications.
- Geospatial systems, spatial databases, or location-based applications.
- Experience building agentic AI assistants in production: setup and tool design, guardrails, natural-language querying and response quality, evaluation, and integration with security and access-control tooling.
- Production data science workflows or infrastructure as code.
- Experience in energy, climate tech, or other infrastructure domains.
Your Approach- Think in platforms: you build things once so others don't have to build them many times.
- Bring a startup builder's bias for shipping: you scope tightly, deliver something real, and iterate.
- Take initiative and turn open-ended problems into working software.
- Balance ambitious ideas with practical engineering judgment.
- Learn from domain experts and help others succeed.
- Explore new ways to build with AI while taking responsibility for results.
LocationWhat We Offer- Competitive salary, performance bonus, and equity.
- Comprehensive health, dental, and vision coverage.
- Hybrid schedule: 3 days in office for collaboration, 2 days remote for focused work.
- Access to leading academic, industry, and government partners in the AI-energy ecosystem.
Salary Range$184,000 - $284,000 Total (Base & bonus)
This range covers Senior and Staff levels; final level, scope, and compensation will be calibrated to your experience and demonstrated impact.
Join us in tackling one of the most important infrastructure challenges of our time - enabling the energy foundation for the age of AI. 
