Data Analytics Manager - Insights

Assurant•Miami, FL

About The Position

We are looking for a Data Analytics Manager – Insights to lead a team responsible for turning complex data into clear, actionable recommendations for senior leaders, internal business partners, and external client audiences. This is a hands-on leadership role for someone who can guide analysts, work directly in the data, shape ambiguous business questions, and deliver trusted insights that influence decisions.

Requirements

Bachelor’s degree in Business Analytics, Statistics, Economics, Applied Mathematics, Computer Science, Information Management, or a related quantitative field; equivalent work experience may be considered.
7+ years of experience in analytics, business intelligence, reporting, data management, research, or a related data-focused field.
2+ years of experience formally leading, supervising, or managing analysts, or equivalent experience leading analytical work through others.
Demonstrated ability to translate complex analysis into recommendations that influence business decisions.
Experience leading analytical projects from intake and scoping through delivery, presentation, adoption, and follow-up.
Experience managing competing stakeholder priorities, team capacity, timelines, quality standards, and delivery expectations.
Advanced SQL skills, including complex joins, window functions, CTEs, performance tuning, data validation, and the ability to work directly with enterprise data warehouses.
Strong Power BI experience, including data modeling, star schema design, DAX, semantic model development, row-level security, report performance tuning, and governed self-service analytics.
Working knowledge of Microsoft Fabric, including Lakehouse/Warehouse concepts, Direct Lake, pipelines, notebooks, or comparable experience with modern cloud/lakehouse analytics platforms.
Strong understanding of data quality, data governance, metric consistency, documentation, access controls, and reporting reliability.
Advanced Excel skills and comfort using modern analytics, automation, and AI-assisted tools to improve productivity and documentation.
Excellent written, verbal, and interpersonal communication skills, with the ability to communicate effectively with technical teams, non-technical stakeholders, senior leaders, and client audiences.
Strong data storytelling skills, including the ability to simplify complex or ambiguous findings into clear messages, business implications, and recommended actions.
Executive presence and confidence presenting to, advising, and being challenged by senior leaders and external stakeholders.
Ability to build trust, influence decisions, manage tradeoffs, and keep stakeholders aligned when priorities shift.

Nice To Haves

Master’s degree in business, analytics, statistics, applied mathematics, operations research, economics, engineering, computer science, or a related quantitative field.
Experience in insurance, telecommunications, retail, warranty, service operations, client operations, or other data-rich operational environments.
Experience with Databricks, Apache Spark, Azure, Python, R, predictive modeling, time series forecasting, segmentation, cluster analysis, retention analysis, or exploratory analytics.
Experience modernizing legacy reporting, consolidating dashboards, defining KPI frameworks, improving semantic models, and increasing adoption of trusted analytics products.
Experience with Agile delivery, product ownership, portfolio prioritization, user acceptance testing, release management, or change management for analytics solutions.
Hands-on use of AI-assisted coding, documentation, or analytics tools to accelerate analysis, automate recurring work, or build internal tooling responsibly.
A track record of developing talent, leading through influence, and creating a culture of curiosity, accountability, inclusion, and continuous improvement.

Responsibilities

Lead analytics delivery and decision support: Lead analytical projects end to end, including problem framing, project planning, data discovery, analysis, validation, insight development, and recommendation delivery.
Translate open-ended business questions from internal leaders and client-facing teams into a clear analytical approach, data plan, and measurable answer.
Use data to identify trends, root causes, risks, opportunities, and performance drivers across business operations, client portfolios, and strategic priorities.
Present complex findings to senior leaders and external client audiences in plain language, with a clear “so what,” recommended actions, and business implications.
Own the quality of what leaves the team, including accuracy, reconciliation, reproducibility, documentation, and consistency of every number shared with clients or executives.
Lead, coach, and develop the team: Lead, coach, and develop a team of analysts, setting clear goals, expectations, priorities, and standards for quality and delivery.
Provide regular feedback, conduct performance and development conversations, and help analysts grow their technical depth, business judgment, communication, and stakeholder impact.
Review analytical approaches, SQL, data models, dashboards, and recommendations to raise the level of the work, not just check completion.
Manage workload and capacity across competing priorities, assigning work based on business value, urgency, skills, and development opportunities.
Support hiring, onboarding, retention, and talent development across the broader Data Analytics organization.
Build scalable analytics practices, governance, and platforms: Drive standardization, automation, and reuse of recurring reporting, dashboards, semantic models, datasets, and analytical assets so the team spends more time on insight and less time on manual assembly.
Advance data modeling, metric definition, documentation, quality controls, and governance best practices across Power BI, Microsoft Fabric, and related analytics environments.
Partner with Data Engineering, IT, Security, business teams, and other analytics groups to ensure solutions are scalable, secure, well-documented, and production-ready.
Establish practical standards for data privacy, access controls, testing, release readiness, issue resolution, and responsible use of AI-assisted analytics tools.
Identify opportunities to modernize legacy reports, reduce duplication, improve performance, and increase adoption of trusted, governed self-service analytics.
Manage stakeholder intake and grow analytics impact: Own intake and prioritization for the team’s work, making tradeoffs visible and aligning requests to business value, urgency, capacity, and leadership priorities.
Proactively engage internal and client-facing teams to identify analytical needs before they arrive as requests and shape them into high-value use cases.
Stay current with trends in insurance, protection and support services, analytics, BI technology, Microsoft Fabric, and responsible AI adoption, bringing forward what is worth testing or scaling.