Description

Data Engineer

Company: Gigapower LLC
Dept./Org.: Trans./Strategy
Location: Virtual (Remote)

Position Summary

The Data Engineer serves as a foundational member of Gigapower's AI organization, responsible for designing, building, and maintaining the cloud data platform that powers analytics, reporting, automation, and AI-driven applications across the business. This role will shape Gigapower's enterprise data architecture from the ground up, integrating operational data sources, establishing scalable data models, and ensuring information is accessible, reliable, secure, and AI-ready. The successful candidate will thrive in a hands-on environment, take ownership of critical data domains, and build the data foundation that enables strategic decision-making and next-generation AI capabilities.

Key Responsibilities
• Design and build scalable cloud-based data models that integrate operational, financial, engineering, and field data into reliable and queryable data assets.
• Develop and maintain data ingestion, transformation, orchestration, and monitoring processes across multiple source systems.
• Create and optimize enterprise data warehouses, marts, and analytical datasets that support reporting, analytics, and AI workloads.
• Model and manage geospatial data, including spatial data types, coordinate systems, and location-based relationships.
• Structure data to support retrieval-augmented generation (RAG), AI search, analytics, and machine learning applications.
• Establish and maintain standards for data quality, governance, lineage, documentation, and confidentiality.
• Partner with IT, Security, and Infrastructure teams to ensure appropriate access controls, reliability, and platform performance.
• Collaborate with business stakeholders to translate operational requirements into scalable data solutions.
• Monitor and optimize data platform performance, reliability, and cost efficiency.
• Utilize AI-powered tools to improve productivity and accelerate data engineering outcomes.

Qualifications
• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Engineering, Mathematics, or a related discipline, or equivalent practical experience.
• 3 to 6 years of experience in data engineering with demonstrated success designing and building enterprise data warehouses, business-focused data marts, and scalable analytical data models
• Strong proficiency in SQL and data modeling principles.
• Hands-on experience with cloud data warehouse platforms such as Snowflake, BigQuery, Redshift, or similar technologies.
• Strong proficiency in Python for data integration, transformation, and pipeline development.
• Experience working with geospatial data, spatial data types, projections, and location-based analysis.
• Experience with cloud platforms, preferably Microsoft Azure, including storage, data pipelines, and serverless technologies.
• Ability to independently own and deliver data engineering solutions in a fast-paced environment.
• Strong communication skills and the ability to partner effectively with technical and non-technical stakeholders.
• Strong analytical, problem-solving, and critical-thinking skills.

Preferred Qualifications
• Experience with dbt or similar data transformation frameworks.
• Experience with orchestration technologies such as Airflow, Azure Data Factory, Dagster, or comparable platforms.
• Familiarity with geospatial or OSP platforms such as Esri and ArcGIS.
• Experience preparing, modeling, and optimizing data for AI, machine learning, and retrieval-augmented generation (RAG) use cases.
• Experience handling confidential, sensitive, or regulated operational data environments.

Key Competencies
• Data Architecture & Modeling: Designs scalable, maintainable, and high-quality data structures that support enterprise decision-making.
• Data Platform Engineering: Builds reliable, secure, and performant data solutions that enable analytics and AI innovation.
• Operational Excellence: Establishes standards for quality, governance, monitoring, and continuous improvement.
• Business Partnership: Collaborates with stakeholders to translate operational needs into valuable data assets.
• Problem Solving & Analysis: Uses data-driven thinking to address complex business and technology challenges.
• Innovation & Automation: Continuously seeks opportunities to improve efficiency through modern data technologies and automation.
• Ownership & Accountability: Takes end-to-end responsibility for delivering high-impact data solutions.