Senior 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/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