Top Senior Data & Analytics Jobs

The Senior Data Analyst will develop and improve Sigma dashboards, reporting, and business intelligence capabilities. Responsibilities include gathering stakeholder requirements, querying and analyzing complex datasets with SQL, improving data quality and governance, defining metrics, automating reporting processes, and partnering on Snowflake data modeling and warehouse improvements. The role also evaluates analytics tools, delivers actionable insights, and communicates findings to technical and non-technical audiences.

Develop and maintain commercial reporting and digital enablement tools for sales, finance, marketing, and operations teams. Provide ad hoc analyses, dashboards, business reviews, trend analysis, and actionable insights to support resource deployment and decision-making. Collaborate with Salesforce product owners on functionality and feature rollouts, coordinate strategic initiatives, and translate complex data into business recommendations.

Conducts research, data analysis, interviews, and reporting to identify eCommerce site performance and customer experience improvements. Manages data and reporting requests, prioritizes work by business impact, defines KPIs with stakeholders, translates business needs into documentation and deliverables, and presents findings to management. Requires SQL, data analysis, eCommerce experience, cross-functional collaboration, and strong communication skills.

Build and maintain business intelligence dashboards, reports, and analytical views in Sigma Computing. Partner with data engineering and business stakeholders to define KPIs, develop scalable data models, improve data quality and governance, and deliver trusted self-service insights. Support Snowflake and dbt-based solutions, automate reporting workflows, and contribute to Agile planning and delivery using Jira and Confluence.

Owns web and conversion analytics across the eCommerce journey. Analyzes customer behavior, funnels, campaigns, merchandising, retention, and commercial performance; builds dashboards and measurement plans; supports experimentation; validates tracking and data quality; develops forecasts and business cases; and presents actionable insights to senior leaders. Partners with cross-functional teams and mentors analysts while establishing analytics standards and best practices.

Designs, builds, and supports scalable data pipelines, integrations, transformations, and services for Mayo Clinic’s biomedical research platform. Works with genomic, omics, clinical, laboratory, imaging, and multimodal datasets; harmonizes data, maintains metadata and provenance, validates quality, and enables governed access. Partners with investigators, bioinformaticians, scientists, and analytics teams to support research, machine learning, and precision medicine applications using Google Cloud and modern data engineering tools.

Develop and administer enterprise Oracle databases for a federal Health IT modernization program. Responsibilities include SQL and PL/SQL development, schema design, performance tuning, ETL and data integration, AWS data services, database migrations, testing, production support, CI/CD, DevSecOps, and secure data practices. The role collaborates with developers, architects, data engineers, and Agile teams while supporting federal compliance requirements and AI-assisted automation.

Sources, prepares, validates, and analyzes data to produce business intelligence reports, analytical datasets, automated processes, and insights. Collaborates with business and technology partners to define needs, improve reporting workflows, and communicate findings. Uses queries, data visualization, automation, and predictive analysis techniques while mentoring junior staff and supporting data governance and compliance.

Leads consumer insights and research initiatives, translating customer experience data, market dynamics, cultural trends, and analytics into recommendations for brand, marketing, product, and business decisions. Develops customer journey maps, conducts qualitative and quantitative research, applies AI-enabled analysis, influences cross-functional teams, builds closed-loop feedback processes, presents decision-ready narratives, and manages research and vendor partnerships.

Lead production data engineering workstreams for AI-enabled financial services solutions. Design Python and SQL pipelines, warehouse and lakehouse models, document ingestion and retrieval-ready data foundations. Implement data quality, lineage, access controls, testing, deployment, monitoring, and recovery processes. Mentor engineers, manage technical delivery, collaborate with clients and vendors, and contribute to reusable assets, proposals, and implementation roadmaps.