Top Senior Data & Analytics Jobs

Designs, develops, tests, troubleshoots, and deploys cloud-based data and analytics solutions using AWS, infrastructure as code, and software development practices. The role involves data integration, automation, application and data model design, system integration, code reviews, testing, troubleshooting, and stakeholder collaboration. It also provides informal leadership through mentoring, design sessions, coding sessions, and communication of complex technical concepts.

Conduct margin, unit economics, market sizing, segmentation, forecasting, KPI reporting, and strategic analysis across GoodLeap’s fintech and sustainable-home-solutions businesses. Use SQL, Python, Excel, and AI tools such as Claude to automate workflows and improve analytical speed. Translate complex findings into executive recommendations, support growth and pricing decisions, track initiative performance, and identify opportunities to embed AI into business processes.

Leads market intelligence by synthesizing market, consumer, competitive, retailer, and industry data into strategic insights. Advises senior leaders, identifies growth opportunities and threats, develops dashboards and executive reports, supports brand and commercial strategy, oversees intelligence tools and providers, and builds organizational analytics capabilities. Requires advanced statistical analysis, pricing models, data visualization, executive communication, and team leadership.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

Designs and manages enterprise Databricks data platforms, including workspaces, clusters, Unity Catalog, SQL warehouses, Terraform infrastructure, and secure data ingestion pipelines. Builds medallion architecture with Delta Lake, supports Genie dashboards and self-service analytics, implements governance and access controls, and maintains CI/CD, testing, documentation, and operational processes. Partners with technical and business stakeholders and supports MCP connectors and agentic data integrations.

Lead global strategic intelligence activities, including market and competitive analysis, industry and geographic assessments, market-share reporting, executive presentations, third-party analyst submissions, and annual market updates. Partner with business units, executives, sales, marketing, finance, and external organizations to develop actionable insights. Manage strategic intelligence projects, improve reporting processes, maintain the market intelligence portal, and support AI and data visualization capabilities.

Manages safety analytics by analyzing and interpreting data, identifying hazards and root causes, validating data integrity, developing dashboards and reports, and recommending corrective actions. Oversees database design, complex queries, data projects, reporting processes, and collaboration with Business Intelligence and corporate data teams. Leads working groups, mentors personnel, manages resources, evaluates technologies, and presents safety trends and insights to senior management for operational decision-making.

Leads the design, development, and operation of Astrana’s enterprise data platform. Builds scalable Databricks, Spark, PySpark, SQL, and Delta Lake pipelines; integrates healthcare data; develops unified data models and APIs; supports Lakebase and PostgreSQL applications; and implements governance, data quality, monitoring, CI/CD, automation, and performance optimization. The role also handles incidents, disaster recovery coordination, engineering standards, code reviews, and mentoring across internal and vendor teams.

Designs, builds, and maintains scalable AWS and on-premises data pipelines supporting genomic data science models. Responsibilities include API ingestion, storage, transformation, data modeling, workflow development, troubleshooting, documentation, infrastructure-as-code, and cross-functional collaboration. The role requires hands-on AWS pipeline development, HPC experience, container orchestration, Python and SQL expertise, CI/CD, secure access management, and production data engineering leadership in a consulting environment.

Leads discovery and requirements gathering for enterprise data solutions, translating business needs into user stories, acceptance criteria, source-to-target specifications, and data workflows. Partners with engineering, architecture, governance, analytics, and business teams throughout delivery. Coordinates validation, reconciliation, testing, defect resolution, and stakeholder approvals while managing risks and dependencies. Documents data lineage, integrations, quality expectations, and reporting needs, and mentors analysts and cross-functional partners.