Senior Manager, Data Operations

Medical Guardian•Philadelphia, PA

2d•Hybrid

About The Position

Medical Guardian is a fast-growing digital health and safety company dedicated to helping people live life without limits. With a strong track record of growth, the company is redefining aging confidently and independently by supporting over 625,000 members nationwide with emergency response systems and remote patient monitoring. The culture is driven by innovation, compassion, and purpose. The Senior Manager of Data Operations will lead the Data Operations function, ensuring enterprise data services, reporting capabilities, and operational processes are reliable, governed, secure, and aligned with business priorities. This role involves establishing the operating model, service expectations, and continuous improvement direction, while advising technology and business leadership. The Senior Manager will manage a team responsible for the current data warehouse, dbt models, business reporting, data quality, and operational analytics. They will collaborate with Data Engineering on evolving data architecture and platforms, maintaining continuity, quality, documentation, and business support. This role acts as the primary operational liaison between business stakeholders and Data Engineering, setting priorities, service levels, governance, and success metrics for enterprise data services. A critical aspect of this role is protecting PHI and sensitive member information through effective people, processes, controls, and partnerships.

Requirements

Sufficient technical depth across the data stack to manage operational performance, evaluate risks and outcomes, and partner effectively with engineering teams.
Strong understanding of modern data warehousing and lakehouse concepts, dbt, dimensional data modeling, semantic layers, and business intelligence delivery.
Ability to assess data-service health, identify operational and data-quality risks, understand technical trade-offs, and work effectively with data engineers and architects.
Practical SQL proficiency sufficient to investigate data issues, validate results, understand data models, and evaluate the quality and usability of delivered data products.
Experience establishing automated and operational data-quality controls, including monitoring, alerting, issue triage, ownership, remediation tracking, and reporting.
Experience applying service-management practices to data products, including service levels, incident management, problem management, change coordination, root-cause analysis, and continuous improvement.
Strong knowledge of data governance, metadata, lineage, access control, privacy, retention, and secure data-handling practices in a regulated environment.
Working knowledge of Git-based version control, CI/CD, automated testing, orchestration, and observability concepts as they apply to analytics engineering and data pipelines.
Experience with reporting and visualization platforms such as Power BI or Tableau, including the governance and adoption of curated dashboards and reporting assets.
Experience with cloud-based data platforms; experience with Azure, Databricks, dbt, or comparable technologies is preferred.
Working knowledge of HIPAA requirements and methods for protecting PHI and other sensitive member data, including least-privilege access, masking or de-identification, auditability, and secure handling throughout the data lifecycle.
Bachelor's degree in Data Science, Analytics, Computer Science, Information Systems, or a related field, or equivalent practical experience; a master's degree is preferred.
8+ years of experience in data operations, analytics, business intelligence, data management, or a related environment, including progressively responsible technical, operational, and leadership responsibilities.
4+ years of direct people-leadership experience, including coaching, performance management, team development, capacity planning, and delivery accountability.
Demonstrated experience managing business-facing data services, reporting operations, data quality, service levels, incidents, priorities, and stakeholder expectations.
Experience overseeing dbt-based analytics or data-modeling work and leading data operations through changes in platforms, architecture, team responsibilities, or operating models.
Strong ability to guide and mentor data analysts, analytics engineers, report developers, or similar data professionals while partnering effectively with data engineers, data scientists, and architects.
Demonstrated success translating organizational objectives into practical data-service priorities, operating processes, and measurable outcomes.
Experience in healthcare and/or retail is a plus; experience operating in a regulated, compliance-driven environment such as HIPAA, SOC 2, or PCI DSS is strongly preferred.
Excellent leadership, written and verbal communication, stakeholder-management, problem-solving, and critical-thinking skills.
Candidates must be able to work from Philadelphia, PA office on Tuesdays and Wednesdays.
Must be legally authorized to work in the United States without current or future employer sponsorship.

Nice To Haves

Master's degree is preferred.
Experience in healthcare and/or retail is a plus.
Experience operating in a regulated, compliance-driven environment such as HIPAA, SOC 2, or PCI DSS is strongly preferred.
Experience with Azure, Databricks, dbt, or comparable technologies is preferred.

Responsibilities

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.