NISC
Sr Manager Platform Development - Data Services (Databricks, AI)
Lake Saint Louis, MO · Senior
Sponsorship not specifiedDetected 29 days ago
DatabricksAWSSparkData EngineeringForecastingCustomer SupportLeadershipCollaborationMentoring
About the role
- Those Members are comprised primarily of 960+ utilities and broadbands across North America.
- The ideal candidate is both a technical leader and a strong people manager who can mentor established teams, foster innovation, and align platform capabilities with the needs of NISC's business and the Members it serves.
Responsibilities
- NISC exists to serve our Members and help them serve their communities through our innovative software products, services and outstanding customer support.
- Drive continuous improvement of CDC pipelines to enhance the speed, quality, and reliability of data moving from operational systems into the cloud lakehouse.
- Oversee the design, development, and optimization of scalable data pipelines, the lakehouse, and analytics frameworks in AWS and Databricks.
- Lead the BI platform modernization and go-forward BI strategy, including the semantic/curation layer and the self-service "bring your own tool" experience for customers.
- Manage resource planning, prioritization, and delivery across multiple concurrent initiatives using a single, transparent intake and prioritization process.
- Partner with senior leadership to translate business and Member needs into technical roadmaps and priorities.
Skills
- 6+ years in data engineering, analytics, or data platform roles, with 4+ years in formal people leadership.
Benefits
- Lead, develop, and mentor technical teams across Data Engineering, CDC, BI, and Data Curation; own hiring, coaching, performance, career growth, and team health.
Company info
- For more than 50 years, NISC has worked to develop technology solutions for our customers, who we call our "Members".
- Our mission is to deliver technology solutions and services that are Member-focused, quality driven and valued priced.
This listing is sourced directly from NISC's careers page and normalized into a canonical job model.