Data Engineer

About the Role

We're looking for a Data Engineer to design, build, and maintain robust data pipelines and warehouse infrastructure that power analytics across multiple business domains including supply chain, education, and energy management projects. This role will work closely with our analytics team and solutioning team to ensure clean, reliable, and well-structured data is available for reporting, financial impact analysis, and business decision-making. This role is foundational to the analytics function, building the pipelines and warehouse infrastructure that Data Analysts rely on for reporting, financial impact analysis, and business insights across our supply chain, education, and energy management projects.

Key Responsibilities

• Design, build, and maintain ETL/ELT pipelines to ingest data from multiple sources (databases, APIs, flat files, third-party systems)
• Architect and maintain data warehouse structures (schemas, tables, models) optimized for analytics use cases
• Ensure data quality, consistency, and integrity across pipelines through validation, testing, and monitoring
• Optimize existing queries, pipelines, and warehouse performance for speed and cost efficiency
• Collaborate with Data Analysts to understand reporting/analytical needs and structure data accordingly
• Build and maintain data models (fact/dimension tables, star/snowflake schemas) to support BI and reporting tools
• Automate recurring data workflows and reduce manual data-handling effort
• Document data pipeline architecture, data dictionaries, and processes for team-wide visibility
• Troubleshoot and resolve data pipeline failures/issues in a timely manner
• Support ad-hoc data infrastructure needs across supply chain, education, and energy management projects as priorities shift

Qualifications

• 2–4 years of experience as a Data Engineer or similar data infrastructure/pipeline role
• Strong SQL skills: query writing, optimization, and schema design
• Hands-on experience building ETL/ELT pipelines (using Python, Airflow, dbt, or similar)
• Experience working with data warehouse platforms (e.g., Snowflake, BigQuery, Redshift)
• Understanding of data modeling concepts (normalization, star/snowflake schema, fact-dimension design)
• Proficiency in Python for scripting, automation, and data transformation
• Familiarity with version control (Git) and basic CI/CD concepts for data pipelines
• Ability to work across multiple projects/domains and adapt to different data sources and business contexts
• Good communication skills to collaborate with analysts and cross-functional stakeholders