Data Engineer

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/Type Script 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.

• 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

• Mid-to-senior data engineer who has owned the design of production data pipelines end to end, working as an autonomous ICDesigning 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/TypeScriptAWS 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/CDStrong English and communication skills

• 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