Data Engineer - Data & AI Applications

Role Description

The Data Engineer plays a key role in supporting analytics, reporting, data workflows, and AI-enabled application development. This role requires strong technical problem-solving, hands-on experience with SQL and Python, the ability to support business-facing reporting and visualization needs, and the capability to help build practical AI-enabled tools, prototypes, automations, and data applications. The ideal candidate is a versatile technical contributor who can work across data engineering, analytics, and application development while collaborating with technical leads, stakeholders, and cross-functional teams to deliver reliable, well-documented solutions.

Key Responsibilities

• Build and maintain data workflows, pipelines, automations, and application components that support analytics, reporting, and AI enabled use cases.
• Support the development of AI enabled tools, prototypes, and applications by preparing data, integrating APIs or AI services, and implementing reusable technical components.
• Collaborate with stakeholders and the Technical Lead to understand data, reporting, application, and AI workflow requirements and translate them into practical technical solutions.
• Design, monitor, and optimize ETL processes, data workflows, and automation scripts for reliability, accuracy, and maintainability.
• Develop and enforce data validation, lineage, quality checks, and documentation across reporting, analytics, and AI application workflows.
• Ensure data is organized, clean, secure, and accessible for analytics, reporting, dashboarding, and application use cases.
• Build, maintain, or support Power BI data models, dashboards, reports, and other business facing data products as needed.
• Create and maintain documentation of pipeline logic, data flow, code, prompts, assumptions, dependencies, and technical decisions.
• Support lightweight user facing tools, prototypes, interfaces, or interactive outputs that help users consume data and AI generated results effectively.
• Work with the Technical Lead to scope assigned work, clarify requirements, identify risks, and align technical decisions with broader project and system priorities.
• Use version control, automation, reusable code patterns, and development best practices to support maintainable delivery.
• Communicate progress, blockers, technical findings, and tradeoffs clearly to team members, stakeholders, and project leadership.
• Proactively identify opportunities to improve reporting processes, data workflows, AI application components, documentation, and delivery efficiency.
• Support data integrity, security, quality, and governance expectations throughout analytics, reporting, and application development work.
• Accurate and timely timekeeping.

Performance Metrics

• Delivers reliable, accurate, and maintainable data workflows, reporting assets, automations, and AI application components on time.
• Supports organization wide projects, client engagements, and internal initiatives across analytics, reporting, data engineering, and AI enabled application development.
• Owns assigned technical workstreams, including requirements clarification, build execution, validation, documentation, stakeholder communication, and handoff.
• Demonstrates practical technical judgment when developing scalable, reusable, and maintainable solutions.
• Maintains clear documentation and reusability of pipelines, scripts, reports, prompts, application components, data flows, and technical decisions.
• Builds and supports data systems, dashboards, reports, and application workflows that are reliable, performant, and useful for business stakeholders.
• Produces clear, accurate, and business consumable reporting outputs, data visualizations, technical findings, and AI enabled deliverables.
• Communicates progress, blockers, risks, assumptions, and technical tradeoffs clearly and proactively.
• Identifies and communicates process improvement opportunities across reporting, data workflows, automation, documentation, and AI application delivery.
• Supports data integrity, security, validation, automation, documentation, and version control best practices.
• Is recognized as a dependable technical owner for assigned systems, workflows, tools, reports, or application components.
• Demonstrates growth toward broader ownership of data and AI enabled solution delivery.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Business Analytics, Engineering, or a related field, or equivalent work experience.
• 5+ years of relevant technical experience across data engineering, data analysis, analytics engineering, application development, automation, business intelligence, or related technical work.
• Proficiency in SQL and Python for data processing, automation, analysis, and application support.
• Experience building or maintaining data pipelines, ETL processes, data workflows, automation scripts, dashboards, reports, or internal tools.
• Experience preparing, cleaning, validating, transforming, and documenting data for analytics, reporting, and application use cases.
• Experience supporting downstream analytics and reporting needs, including Power BI dashboards, reports, data models, or similar business intelligence products.
• Familiarity with AI enabled tools, generative AI workflows, LLM based applications, prompt workflows, AI assisted automation, or API based AI service integration.
• Experience integrating APIs, databases, files, external services, or structured and semi structured data sources into technical workflows.
• Understanding of database management, data modeling, data quality, data validation, and data integrity best practices.
• Demonstrated use of version control, documentation standards, reusable code practices, and process automation.
• Ability to own assigned technical workstreams with minimal oversight, including requirements clarification, build execution, validation, documentation, and handoff.
• Ability to communicate technical information, data findings, limitations, risks, and recommendations clearly in writing and verbally.
• Strong analytical thinking, problem solving skills, attention to detail, and continuous improvement mindset.
• Experience collaborating effectively with Technical Leads, analysts, developers, stakeholders, and cross functional or matrixed teams.

Preferred Skills and Qualifications

• Exposure to front end or user interface technologies such as HTML, JavaScript, AngularJS, jQuery, or similar technologies.
• Experience with tools such as Airflow, dbt, or similar orchestration and transformation frameworks.
• AWS, Azure, Microsoft Fabric, or other cloud data platforms (e.g., Snowflake, Databricks) a plus.
• AWS Cloud Practitioner, AWS Solutions Architect, or equivalent certification.
• Generative AI (GenAI) and machine learning (ML), including retrieval augmented generation, embeddings, vector databases, structured outputs, tool calling, agents, or evaluation workflows.
• Energy utility domain knowledge.

Benefits

• Salary: $120,000-$135,000+ depending upon qualifications and experience.
• Work from home (fully remote).
• 2 weeks paid time off.
• Home equipment allowance (monitors, stand-up desk, etc.).
• Annual education allowance.
• 401(k) with company matching.
• Medical, Dental, Vision, and Life insurance.