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Chicago, Illinois, United States
Onsite
The Opportunity
We have built a data platform on Microsoft Fabric and Azure. Our analytics team is consuming data, and our AI roadmap depends on clean, structured, trustworthy data to deliver. What we are missing is the technical owner who makes the data actually work.
This is not a hands-off leadership role. The Director of Data Engineering will be a player-coach: leading and growing a team of 2–5 engineers while remaining a hands-on contributor who can architect solutions, write pipelines, and set the technical bar. You will own the Fabric platform end-to-end, connect our source systems into a reliable data foundation, and serve as the linchpin between raw data and every downstream initiative that depends on it.
Why This Role Matters
Every AI project on our roadmap requires clean, well-structured, trustworthy data. The Fabric platform is in place. This role makes it useful. Without strong data engineering leadership, our analytics team is constrained by data quality issues they cannot fix — and our AI initiatives stall before they start.
Own the Microsoft Fabric Platform
Serve as the technical owner of JSSI's Microsoft Fabric environment — architecture, governance, performance, and access control
Drive the roadmap for the platform including CI/CD enablement, Git integration, and centralized ingestion workspace design
Manage Fabric capacity, right-sizing, backup, and disaster recovery
Build and Maintain Data Pipelines
Design, build, and operate reliable data pipelines that connect source systems into clean, usable data
Establish monitoring, alerting, and SLA standards for pipeline reliability
Define the Data Foundation
Establish a single source of truth across all systems and acquisitions, ensuring every team is working from the same numbers
Build and maintain enterprise semantic models that serve analytics, reporting, and AI use cases
Partner with the data governance function to implement Microsoft Purview for cataloging, classification, and data privacy standards
Enable Analytics and AI
Make the analytics team faster by solving data quality and availability issues at the source
Build the data foundation that AI initiatives depend on, enabling the organization to move from insight to action
Develop and support AI data agents to expand analytics delivery across the organization
Responsible for supporting ML initiatives (not just AI agents)
Lead and Grow the Team
Manage and mentor a team of 2–5 data engineers, setting technical standards and fostering a high-performance culture
Partner cross-functionally with analytics, product, and business stakeholders to prioritize and deliver
Represent the data engineering function in leadership discussions and help shape JSSI's broader data strategy
Experience
10+ years of experience in data engineering, with a track record of building and scaling data platforms in production environments
3+ years in a leadership role managing engineers, with demonstrated ability to develop talent and build team culture
Experience as a player-coach — comfortable rolling up your sleeves while also setting direction for the team
Technical Skills
Deep expertise with Microsoft Fabric, including Lakehouse and Warehouse architecture, pipelines, and OneLake
Strong hands-on experience with Azure cloud infrastructure and Azure Data services
Proficiency in SQL and Python for data engineering and pipeline development
Experience designing and implementing ETL pipelines across complex, multi-source environments
Familiarity with CI/CD practices and Git-based deployment workflows for data platforms
Experience with data governance platforms (Microsoft Purview preferred) and semantic layer design
Working knowledge of Power BI and enterprise reporting architectures
Familiarity with AI/ML data patterns and building data products that support machine learning use cases
Leadership and Collaboration
Strong communicator who can translate technical complexity into clear language for business stakeholders
Collaborative partner who works well across analytics, product, and business teams
High ownership mindset — you treat data as a product and hold yourself accountable for its quality and reliability
