Top Senior Data & Analytics Jobs

Own and scale end-to-end data infrastructure: build production ETL pipelines, design scalable schemas, enforce data quality/governance/security, create self-serve data models for analytics and ML, instrument observability, and partner cross-functionally while participating in on-call incident response.

Leads advanced data analysis and builds scalable Tableau dashboards and reporting solutions. Develops dbt data models, collaborates with data engineering teams on Snowflake structures and pipelines, and translates business needs into analytical solutions. Ensures data quality through testing and validation, improves analytics processes, explores advanced analytics and AI opportunities, and mentors junior analysts while supporting strong stakeholder engagement and delivery standards.

Build and maintain data pipelines to ingest, normalize, and deliver messy healthcare data (FHIR, HL7, CCDAs, PDFs) into structured models for AI and clinical use. Implement ETL, real-time webhooks, document extraction, PII sanitization, storage on AWS, and monitor data quality while ensuring compliance with healthcare interoperability standards.

Build and operate trusted data products, governed datasets, scalable pipelines, and platform workflows supporting analytics, AI, and business operations. Responsibilities include data quality, validation, observability, recoverability, semantic consistency, governance controls, permissions, privacy-aware access, automation, and production monitoring. The role partners with stakeholders to resolve ambiguous data needs, drives platform improvements, provides technical direction, and adopts agentic development workflows.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

Leads Columbia’s North America Commercial Analytics function, unifying reporting, metrics, data sources, and decision-making across wholesale, DTC, marketing, merchandising, and consumer insights. Builds analytics capabilities, operating rhythms, and high-performing teams while translating commercial, consumer, market, and financial data into growth and profitability recommendations. Partners with technology teams on analytics platforms and champions AI-enabled decision support. Advises executive leadership through strategic analysis and fact-based insights.

Build and operate streaming and batch data pipelines for energy-storage telemetry. Design reliable, query-ready time-series and event datasets using open table formats, orchestration frameworks, AWS, and infrastructure-as-code. Lead technical designs, partner with field operations and engineering teams, support on-call operations, and mentor engineers while improving data reliability and engineering practices.

Own Serval’s complete data function as its first dedicated data hire. Build a unified warehouse, ETL/ELT pipelines, data models, quality and governance systems, documentation, and self-serve analytics. Partner with technical and business teams to define trusted metrics, consolidate product and SaaS data, and create AI-powered internal data tools. Select the data stack, establish engineering standards, and support scalable, cost-conscious infrastructure.

Leads College Board’s Operations analytics strategy and team, developing forecasting, predictive modeling, optimization, cost assessment, and decision-support capabilities. Partners with senior leaders across Operations, Product, Program, Technology, and Finance to improve execution, customer experience, efficiency, and cost savings. Responsibilities include setting technical direction, embedding analytics into operational decisions, presenting insights, hiring and coaching analytics staff, and building future AI and data-infrastructure capabilities.

Lead design and delivery of Snowflake-based data solutions, data models, ETL pipelines and data warehouse applications. Provide architecture direction, drive technology transformation and POCs, evangelize modern data architecture, collaborate with global stakeholders and Agile teams, and apply advanced analytics (ML/AI/GenAI) while ensuring data security and PII management.