Top Hybrid Data & Analytics Jobs

Lead consumer lending analytics and strategy across credit card, HELOC, and Auto/RV products. Evaluate portfolio performance, member behavior, pricing, targeting, risk, profitability, and growth opportunities. Develop strategic recommendations, forecasts, business cases, and analytical frameworks using SQL and large datasets. Partner with finance, credit risk, marketing, operations, and technology leaders while managing, coaching, and developing an analytics team.

Leads consumer and professional research supporting Design-to-Value initiatives and product innovation. Designs and executes qualitative, quantitative, advanced analytics, and AI-driven studies; integrates primary, secondary, competitive, and category data; and translates findings into actionable recommendations balancing customer value, product performance, quality, and cost. Partners with product management, engineering, design, marketing, Agile teams, and external research agencies to inform product decisions, testing, refinement, and measurable business outcomes.

Leads enterprise data platform architecture, engineering standards, automation, security, governance, observability, and cloud integration. Designs scalable batch and streaming pipelines connecting SAP, legacy systems, Databricks, and enterprise data products. Guides SAP transformation data harmonization, analytics and reporting strategy, infrastructure as code, CI/CD, reliability, and platform roadmaps. Provides hands-on technical leadership, mentors senior engineers, collaborates across business and transformation teams, and supports critical incidents.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

Analyze business operations and complex datasets to identify trends, opportunities, and challenges. Use SQL, statistical methods, hypothesis testing, and Power BI to develop dashboards, executive reports, and actionable recommendations. Translate business requirements into analytical frameworks, design tests measuring incremental growth, collaborate with cross-functional stakeholders, and lead projects from initiation through completion.

Build and govern reporting, dashboards, workflows, and KPI tools; analyze data to identify trends, risks, root causes, and improvement opportunities; and provide actionable recommendations. Lead Provider Experience PMO initiatives from requirements gathering through launch and follow-up. Partner cross-functionally to improve processes, document requirements and governance, support strategic programs, and enable scalable provider network growth.

Supports Fund Management through regulatory communications, cost allocation, operational reporting, and analytics. Develops and maintains Access, Excel, SQL, and enterprise reporting solutions; integrates data across systems; improves data quality and workflows; and supports testing, validation, deployment, automation, and AI-enabled process enhancements. The role also serves as a technical subject matter expert and coordinates regulatory reporting while driving efficiency and cost savings.

Analyze complex datasets, develop data models, dashboards, and visualizations, and conduct predictive and statistical analysis for client decision-making. Collaborate with clients to deliver tailored analytics solutions, maintain data integrity, communicate insights through reports and presentations, and support data-driven operational strategies using SAS, Power BI, Tableau, and SQL tools.

Own growth and product analytics across activation, retention, adoption, and expansion. Analyze pricing, packaging, monetization, cohorts, and experiments; build instrumentation, measurement infrastructure, AI-assisted scripts, agents, and automation; and turn insights into shipped product or growth changes. Partner with growth, product, pricing, and engineering while establishing data quality standards and trusted metric documentation.

Analyze transaction enrichment product performance, data quality, accuracy, match rates, and other metrics. Investigate anomalies in large datasets, build dashboards and automated alerts, improve QA processes and workflows, and develop documentation and monitoring standards. Partner with Product, Engineering, and AI/ML Data Science teams to translate insights into product improvements and automate recurring analytical work.