Data Engineer

Project Description:

Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028.

Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an autonomous data engineer to strengthen our data foundation through good modeling, clean schemas, reliability and automation.

Responsibilities:

Work closely with product teams to help define and execute technical criteria

Architect and maintain robust data ingestion pipelines that feed both our application and analytics

Design well-modeled, layered tables with clean schemas that turn raw data into analytics-ready data

Help onboard client data from a variety of sources, such as Databricks via Delta Sharing, and integrate it with our existing services and workflows

Make pipelines reliable and automated: data quality tests, monitoring, safe reprocessing and schema change handling

Build a documented, well-tested data foundation that serves the app, analytics and future AI use cases

Mandatory Skills Description:

Mid-to-senior data engineer who has owned the design of production data pipelines end to end, working as an autonomous IC

Designing and building production ingestion pipelines from APIs, files and shared-data sources, using a workflow orchestrator (any tool, e.g. Airflow, Dagster or Step Functions)

Production experience with a cloud data warehouse (Snowflake preferred)

Strong SQL, plus production experience in Python (preferred) or Node.js/TypeScript

AWS or another enterprise cloud platform

AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CD

Strong English and communication skills

Nice-to-Have Skills Description:

dbt

Preparing data for AI/LLM use (e.g. semantic layers, metadata, retrieval-ready datasets)

Infrastructure as code (e.g. Terraform)

Exposure to energy, utilities or sustainability data