Top Senior Data & Analytics Jobs

Own analytical projects supporting revenue, profitability, and operational performance for the insurance business. Analyze funnels, campaigns, conversions, costs, and trends; build SQL queries and Looker dashboards; automate reporting with Python; define KPIs; validate data; and present actionable recommendations to stakeholders and leadership.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, segmentation, and LTV analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and deliver actionable recommendations. Requires strong SQL and Python skills, causal measurement expertise, marketing platform experience, and the ability to communicate insights to senior stakeholders.

Leads a centralized contact center analytics function, managing analysts, prioritizing analytical requests, and setting operating standards. Performs complex analyses and executive insight reporting while building repeatable frameworks, automation, and responsible AI-assisted workflows. Partners with reporting and technology teams on data access and tools including MicroStrategy, Genesys, CallMiner, and Snowflake. Ensures analyses account for TCPA, FCRA, and FDCPA requirements while supporting strategic initiatives across customer service, lending, collections, complaints, workforce management, and related operations.

Architects and leads enterprise data engineering solutions, including data warehouses, master data management, data lakes, ETL pipelines, integration services, and data processing systems. Oversees end-to-end data solutions, troubleshoots complex engineering issues, ensures global data integration standards, and mentors developers, vendors, consultants, and partners. The role also supports data scientists and engineers through efficient database, warehousing, and data access architectures.

Develop and optimize Apache NiFi ETL workflows supporting a geospatial visualization platform. Manage data pipelines into PostgreSQL, integrate APIs and external systems, query large datasets with SQL, and transform data using Python. Monitor and troubleshoot AWS, PostgreSQL, and NiFi infrastructure; support data quality, database performance, geospatial data modeling, containerized development, Git-based workflows, and collaboration with analysts.

Leads site data, integration, and systems capabilities within a regulated manufacturing environment. Owns data strategy, governance, standards, system integrations, historians, data platforms, application lifecycle management, and AI enablement. Ensures compliant, reliable, contextualized manufacturing data while maintaining validation and inspection readiness. Manages the data enablement roadmap, resolves data quality and connectivity issues, supports analytics and AI deployments, and leads technical teams, contractors, and cross-functional stakeholders.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and ETL/ELT workflows. Build trusted data products for analytics, automation, and AI agents; translate ambiguous business needs into scalable solutions; lead cross-team initiatives; ensure reliability and data quality; mentor engineers and analysts; and develop, evaluate, and monitor production LLM and agent systems using Python and SQL.

Leads Motorsports Data Infrastructure and Data Science teams while owning the strategy, architecture, and delivery of data platforms supporting telemetry, simulation, trackside, analytics, and AI/ML use cases. Responsibilities include managing managers, designing streaming and batch pipelines, productionizing ML models, setting engineering standards, managing Azure and Databricks capacity, protecting sensitive data, and partnering with racing and executive stakeholders. The role remains hands-on in architecture, coding, code review, incident support, and technical problem-solving.

Build and operate the Global Card team’s data foundation, including pipelines, schemas, data marts, feature pipelines, quality monitoring, and data contracts. Lead architecture and technical direction, partner with product, analytics, and data science teams, contribute to backend services, establish engineering standards, mentor colleagues, and ensure reliable, secure, scalable data systems.

Develop and optimize advanced SQL transformations, stored procedures, ETL workflows, and data-quality processes for large Medicaid claims and health-related social needs datasets. Build reproducible, documented data pipelines; calculate healthcare quality, equity, and disparity metrics; conduct code reviews; and collaborate with stakeholders to turn complex data into visualizations and policy insights.