Top Hybrid Data & Analytics Jobs

Analyze complex data to support emerging payments and AirKey initiatives. Build data solutions, self-service tools, dashboards, metrics, and visualizations; provide business insights; and support data quality, metadata, lineage, security, and access governance. Collaborate with business and technology teams while using Python, R, Spark, SQL, Snowflake, AWS, and data governance tools.

Analyze complex data to support business decisions, build self-service data solutions, develop dashboards and metrics, and improve data quality and governance. The role uses Python, R, Spark, SQL, AWS, business intelligence tools, and data management practices while partnering with business and technology teams on analytics and transformation initiatives.

Leads enterprise-wide validation of analytical tools, including planning validation projects, assessing methodology quality and tool risk, researching best practices, and communicating findings through reports and presentations. Partners cross-functionally to influence business strategy, handles concurrent projects, develops solutions to business problems, and coaches junior staff. The role requires substantial data analysis, statistical or econometric modeling, large-scale data manipulation, open-source technology experience, and people management.

Supports digital customer-experience analysis by gathering research, customer behavior data, competitive intelligence, and performance metrics. Assists with reports, project plans, ticket submissions, presentations, and cross-functional execution. Uses Adobe Analytics and other reporting tools to develop actionable insights, support strategic initiatives, and improve customer satisfaction, loyalty, retention, and business growth through Lowe’s Launchpad rotations and technology training.

Owns Sales and Servicing data as a trusted, governed, discoverable, and AI-ready enterprise asset. Defines data standards, quality controls, metadata, lineage, and business definitions while resolving complex data issues. Partners with AI, engineering, product, governance, and business leaders to enable sales effectiveness, client insights, servicing automation, intelligent routing, and self-service use cases. Leads cross-functional decision-making and translates complex data concepts into actionable business recommendations within a regulated financial services environment.

Serve as the Deposits, Liquidity, and Pricing & Rates data domain expert. Establish trusted data definitions, lineage, quality, governance, discoverability, and AI readiness. Partner with AI, engineering, product, and governance teams to support forecasting, funding, account visibility, and pricing use cases. Lead data issue resolution, platform integrations, cross-functional decision-making, and strategic initiatives while translating technical data concepts into business recommendations and ensuring risk and compliance requirements are met.

Owns Sales and Servicing data as a trusted, governed, discoverable, and AI-ready enterprise asset. Defines data quality standards, metadata, lineage, and business definitions while resolving complex data issues. Partners with AI, engineering, product, governance, and business leaders to enable analytics, automation, intelligent routing, and self-service use cases. Translates technical data concepts into business recommendations and leads cross-functional decision-making in a regulated financial services environment.

Owns complex GenAI Standards of Care evaluations, reviewing architectures, data sources, testing, controls, and governance evidence. Identifies risks and control gaps, provides remediation guidance, supports governance dashboards, escalates systemic issues, and improves governance processes and documentation. The role partners with technical teams, solution owners, and leadership to ensure GenAI use cases meet enterprise standards and manage data and technology risks.

Analyze go-to-market performance across Sales, Marketing, and Client Success, including pipeline, funnel conversion, campaigns, retention, and revenue drivers. Build and maintain dbt models and data marts using Salesforce and modern data-stack tools. Own data quality, documentation, BI-as-code contributions, and AI-enabled analytics workflows. Partner with stakeholders to turn ambiguous business questions into actionable recommendations and deliver results in agile two-week sprints.

Maintain global ERP master data, item and branch records, BOMs, planning parameters, MRP data, and supply chain documentation. Analyze data quality issues, support reporting and dashboards, ensure compliance with SOX and GMP requirements, and collaborate with Manufacturing, Quality, Regulatory, Finance, and planning teams. The role also contributes to process improvements, ERP enhancements, and transformation initiatives.

Analyze and optimize drug pricing using data-driven models, dashboards, reports, and strategic frameworks. Partner with Sales, Operations, Marketing, Finance, Product, pharmacy partners, and data teams to identify opportunities, define success metrics, improve reporting, and balance pharmacy economics with patient affordability. Serve as a trusted analytical advisor and support backend data development and query optimization.

Develops Python data pipelines and business data objects for applications. Designs, implements, tests, and deploys software and CI/CD pipelines, performs source-to-target mapping, resolves complex technical issues, and promotes software quality practices. The role requires collaboration across functional teams, independent execution of complex assignments, and experience with cloud platforms, Snowflake, version control, and AI coding tools.

Build and maintain scalable fraud data pipelines, Snowflake data marts, dbt models, and summarized metric tables. Partner with fraud analysts and engineering teams to enable reporting, scorecards, monitoring, and operational decision-making. Establish data engineering standards, documentation, testing, governance, data quality, and pipeline reliability for audit-ready fraud analytics infrastructure.

Develop causal economic analyses using Mastercard and public datasets. Translate business questions into empirical designs, apply econometric and machine learning methods, lead projects from data preparation through validation, guide causal inference practices, prototype scalable analytics, and communicate findings to clients, stakeholders, and nontechnical audiences.