Fabric Data Engineer

Lasting Change Inc•Fort Wayne, IN

About The Position

The Fabric Data Engineer is an integral member of Lasting Change's data platform team, contributing hands-on to the development, optimization, and governance of Petra, the organization's enterprise data lakehouse built on Microsoft Fabric. This role focuses on building reliable, performant data pipelines and analytical data products that drive meaningful business insights across the organization. The Fabric Data Engineer brings a strong builder mindset — equally comfortable writing high-quality transformation code, establishing agentic AI-assisted development workflows, and communicating clearly with business stakeholders to deliver solutions with measurable impact. This role is a key contributor to Lasting Change's mission of becoming a data-driven organization.

Requirements

Perform quality work within deadlines with or without direct supervision.
Interact professionally with other employees, customers, and clients.
Work effectively as a team member.
Work independently while understanding the necessity for communicating and coordinating work efforts with other employees and organizations.
Exhibit exceptional integrity in all matters.
Lead by example.
3–5 years of professional experience in data engineering, ETL orchestration, and multi-system data integration.
Hands-on experience with cloud data platforms; Microsoft Fabric, Azure (Synapse Analytics, Data Factory), or Databricks strongly preferred.
Solid understanding of data lakes, data warehouses, and lakehouse architectures.
Working knowledge of Medallion Architecture design and implementation.
Proficiency in Python, PySpark, Spark SQL, Delta Lake, and T-SQL.
Experience with data modeling and dimensional modeling to support analytics and reporting.
Demonstrated use of AI tools to enhance development velocity, code quality, and documentation.
Strong communication skills with the ability to articulate technical concepts and business value clearly to varied audiences.
Highly organized, detail-oriented, and committed to advancing the organization's mission.
Use of Fingers
Feeling
Speaking
Hearing
Repetitive Motions
Capable of making sound decisions by use of reasonable and logical judgments.
Demonstrated competence in understanding, interpreting, and communicating procedures, policies, information, ideas, and instructions.

Nice To Haves

Experience building and maintaining Power BI reports and semantic models.
Microsoft Fabric, Azure, or other cloud platform certifications.
Experience with Microsoft Fabric or Databricks as a primary platform.
Familiarity with dbt or other data quality and transformation frameworks.
Experience in a nonprofit or mission-driven organizational context.
Familiarity with agentic AI development workflows and/or MCP (Model Context Protocol) integration.
Commitment to continuous learning and professional growth.

Responsibilities

Design, implement, and manage ETL/ELT workflows within Microsoft Fabric, ensuring efficient data ingestion, transformation, and delivery.
Build and maintain Delta Lake solutions within a Medallion Architecture framework (Bronze, Silver, Gold layers), applying best practices for data quality, validation, and deduplication.
Integrate data from multiple internal and external systems using cloud-native services.
Monitor pipeline performance, proactively troubleshoot issues, and resolve bottlenecks.
Collaborate with analysts and stakeholders to understand business requirements and translate them into scalable technical solutions.
Develop analytical and dimensional data models optimized for reporting and self-service analytics.
Build and maintain Power BI semantic models, reports, and dashboards as needed.
Ensure datasets are well-documented, discoverable, and reliable for downstream use.
Communicate findings and data product status clearly to both technical and non-technical audiences, with a consistent emphasis on business value.
Establish and operate agentic AI-assisted engineering workflows that generate the majority of transformation, pipeline, and documentation code upfront.
Leverage AI tooling to increase development velocity, improve code quality, and enhance consistency across data products.
Continuously refine AI usage patterns while maintaining quality, security, and governance standards.
Contribute to a culture of disciplined, high-output engineering through intentional use of AI tools.
Maintain comprehensive technical documentation for all ETL processes, data logic, and system architecture.
Conduct testing and validation of data flows to ensure accuracy and compliance with data governance standards.
Contribute to and adhere to established coding, architecture, and documentation standards.