General Responsibilities:
The Senior Manager of Data Operations leads the organization’s Data Operations function and ensures that enterprise data services, reporting capabilities, and operational processes are reliable, governed, secure, and aligned with business priorities. The role establishes the operating model, service expectations, and continuous-improvement direction and advises technology and business leadership.
The Senior Manager will lead a team of individual contributors supporting the organization’s current data warehouse, dbt models, business reporting, data quality processes, and operational analytics. As the company’s data architecture and platforms evolve, the Senior Manager will partner closely with Data Engineering to maintain continuity, quality, documentation, and effective business support.
The Senior Manager will serve as the primary operational bridge between business stakeholders and Data Engineering, establishing priorities, service levels, governance practices, and measures of success for enterprise data services. Protecting PHI and other sensitive member information remains a critical responsibility.
To advance these objectives, the Senior Manager of Data Operations will lead the people, processes, controls, and partnerships needed to deliver reliable and trusted data services. Key responsibilities include:
•
Lead, coach, and develop Data Operations individual contributors while establishing the team’s operating model, service roadmap, priorities, standards, measures of success, and continuous-improvement direction.
•
Own portfolio intake, capacity planning, prioritization, coordination, and status communication for business reporting, dashboard, and operational data requests, balancing business value, risk, urgency, and available resources.
•
Oversee the operational management of the organization’s data warehouse and dbt-based modeling processes, ensuring reliable delivery, documentation, quality, and support.
•
Serve as the senior operational representative for Data Operations and business-service requirements in data architecture and platform discussions, influencing priorities and operating decisions with Data Engineering and Enterprise Architecture.
•
Partner with Data Engineering to establish clear operational and technical ownership for data products, models, pipelines, documentation, quality controls, lineage, and ongoing support.
•
Translate business needs into clear requirements, priorities, acceptance criteria, and operational expectations for Data Engineering and other technology partners.
•
Establish and monitor service-level objectives for critical reports, dashboards, datasets, and operational data processes, including availability, freshness, completeness, accuracy, and delivery timeliness.
•
Coordinate data incident response, stakeholder communication, root-cause analysis, corrective actions, and follow-through for data-service failures and recurring quality issues.
•
Maintain an inventory of critical data products, business owners, technical owners, dependencies, authoritative sources, lineage, and service expectations.
•
Partner with business stakeholders to define and govern key performance indicators, semantic definitions, and trusted reporting sources, reducing duplicate or conflicting metrics and data models.
•
Oversee business reporting and visualization practices so complex information is communicated clearly and consistently to executive and functional stakeholders, including Finance, Marketing, Operations, and other business areas.
•
Coordinate business acceptance testing and validation for new or materially changed data products, reports, dashboards, and operational processes.
•
Develop and manage data governance and operating procedures in partnership with Security, Privacy, Compliance, Data Engineering, and Enterprise Architecture, including data quality, access, documentation, lineage, retention, and secure handling of PHI and other sensitive data.
•
Provide executive-level reporting on Data Operations performance, business impact, risks, capacity, and priorities, including reliability, data quality, incident trends, delivery time, backlog health, and adoption of trusted reporting assets.
•
Manage relationships with external data vendors, partners, and consultants as needed.