Data Engineer

Description

Description
Display Goodwill’s Core Values – Respect, Excellence, and Integrity. The Staff Data Engineer serves as a technical contributor within Goodwill KY’s Data Intelligence team, leading the design, development, and optimization of the organization’s end-to-end data ecosystem. This role focuses on delivering scalable data pipelines, analytical data models, and foundational data products that support business intelligence, reporting, data science, and AI-driven operational automation.

Key responsibilities include architecting ETL/ELT processes, developing enterprise data models, and collaborating with business stakeholders to transform complex workflows into well-structured, trustworthy datasets. The role also involves documenting system logic, supporting Power BI and broader BI initiatives, and contributing to AI workflow development through tools such as Copilot Studio and Power Automate.

The ideal candidate is an experienced engineer with strong technical depth in Python, SQL, and data modeling, capable of working independently and providing architectural guidance. This position requires strong communication skills, a process-oriented mindset, and the ability to translate business needs into sustainable, resilient data solutions that elevate the organization’s analytical capabilities.

• Design, build, and maintain scalable ETL/ELT pipelines using Python, SQL, and cloud-based tools.
• Develop modular, well-documented code in a Git-based environment with appropriate quality checks and monitoring.
• Support the migration and integration of legacy systems into unified, scalable data platforms.

• Design and maintain enterprise data models, including star schemas and curated semantic layers for BI.
• Create ERDs, process maps, and documentation to validate business workflows and ensure consistent data standards.
• Collaborate with analysts to optimize datasets for Power BI and executive reporting.

• Develop AI-assisted workflows using tools such as Copilot Studio and support automation through Power Automate.
• Prepare structured datasets for analytics and light machine learning needs, including feature engineering and exploratory analysis.
• Identify opportunities to embed automation and AI into operational processes across departments.

• Lead discovery sessions to understand stakeholder requirements and translate them into technical specifications.
• Communicate complex technical topics clearly for non-technical audiences and provide mentorship to team members.

• Document business rules, transformation logic, and system flows