Remote Data Engineer - Energy AI Platform

About the role

A client of DXC Luxoft, an energy solutions provider, is building an AI-enabled platform that supports operations, tracking and decision-making for large-scale renewable and traditional energy portfolios. The client's MVP is due in February 2027 and the project continues into 2028. The role is a remote position in Ukraine within the Data Platform Engineering team, focused on strengthening the data foundation through good modeling, clean schemas, reliability and automation.

Key responsibilities

• Work closely with product teams to help define and execute technical criteria.
• Architect and maintain robust data ingestion pipelines that feed both the application and analytics.
• Design well-modeled, layered tables with clean schemas that turn raw data into analytics-ready data.
• Onboard client data from a variety of sources, such as Databricks via Delta Sharing, and integrate it with 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 serving the app, analytics and future AI use cases.

Required profile

• Mid-to-senior data engineer who has owned the design of production data pipelines end to end, working autonomously as an individual contributor.
• Experience 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.
• Experience with AWS or another enterprise cloud platform.
• 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

• dbt
• Preparing data for AI/LLM use (semantic layers, metadata, retrieval-ready datasets)
• Infrastructure as code (e.g. Terraform)
• Exposure to energy, utilities or sustainability data