Staff Platform Engineer

Palmetto Clean Technology•Charlotte, MA

2d•Onsite

About The Position

Energy is one of the defining challenges and opportunities of our generation, and the companies that lead will be the ones that operationalize AI the fastest. Palmetto is building to be agent-first and machine-first: data, systems, and products designed for machines, models, and agents, not just people reading dashboards. The mandate is to take what is cutting edge today and make it our table stakes. We are looking for a versatile, senior engineer to help build the services and infrastructure that make this real. You will build robust, containerized services that make data across many systems discoverable, governed, and usable by people, applications, and AI agents, bring new data into that ecosystem, and integrate it for the teams that rely on it. This is a broad, hands-on role for someone who is fluent in data but is fundamentally a software and platform engineer, not a traditional data engineer focused on the warehouse. Priorities will shift as the business does, so we want someone who can move across services, data, infrastructure, governance, and AI tooling, and pick up whatever is next. Success means agent-first and machine-first stop being aspirations and become how Palmetto operates by default: services, teams, and AI agents get the data they need, governed and reliable, wherever the business goes next. You help make the frontier our foundation, and put Palmetto at the front of an AI-native energy industry, on a challenge that defines our generation.

Requirements

Senior-level experience (typically 6+ years) building and operating production backend services or platforms.
Polyglot engineer with deep Python plus working JavaScript/TypeScript and SQL, and strong command-line skills (bash/zsh).
Proven track record shipping robust, containerized microservices (FastAPI with Pydantic in Python, or NestJS on Fastify with Zod in TS), with typed API contracts over GraphQL and/or REST.
Cloud infrastructure experience, ideally multicloud, across heterogeneous data estates.
Fluency with the modern data stack as an integrator and for building data transformations (Snowflake, dbt, SQL, Airflow), plus open-source data tooling (DuckDB, Iceberg, and similar); comfortable wrangling messy data into usable form.
Strong authentication and authorization knowledge; ideally hands-on with fine-grained authorization (FGA) as an operational enforcement capability in applications, across models such as ReBAC and ABAC.
A DevOps and SRE sensibility (CI/CD, observability) and a working understanding of machine learning principles.
An agentic, AI-forward builder and strong communicator who enjoys solutions engineering and can pivot as priorities change.

Nice To Haves

Experience with data catalogs or metadata platforms and data governance.
Experience building or integrating AI-agent interfaces (e.g., MCP).
An eye for communicating data through data apps and visualizations. Information-design judgment matters more than any one tool (D3, charting or BI libraries, front-end frameworks, or directing AI tools to build them).
Familiarity with GraphQL frameworks, Redis, secrets management, and Kubernetes.
Experience sourcing or licensing third-party datasets.
Genuine interest in clean energy and home electrification.

Responsibilities

Build and operate robust, containerized backend services and APIs (REST, GraphQL) that serve data from many systems.
Integrate heterogeneous, multicloud data sources, and find, ingest, transform, and wrangle new datasets into clean, usable form.
Build governed access to data: authentication, fine-grained authorization, audit, and policy enforcement.
Design for machines first: expose data and capabilities to AI agents, services, and automated callers (for example, via MCP), so systems can act on data, not just people read it.
Stand up and maintain metadata, cataloging, and lineage so data stays discoverable and trustworthy.
Own services in production: CI/CD, observability, performance, reliability, and cost.
Act as a hands-on technical and solutions-engineering partner for internal teams (and at times external ones) on integrations, including standing up data apps and visualizations that communicate data value to stakeholders.
Support ML and AI workflows by making features and model outputs discoverable, governed, and servable.