Top Remote Senior Level Data & Analytics Jobs in Boston, MA

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Lead CX analytics strategy and operations by standardizing metrics, enabling self-service analytics, and embedding AI in the analytics lifecycle to enhance decision-making processes.

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

Partner with People teams to turn HR and org data into actionable insights. Build complex queries, statistical analyses, dashboards, and a centralized people data repository. Drive data governance, data quality, and stakeholder communication to support hiring, retention, productivity, and workforce decision-making.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

The Lead Data Analyst (Network) drives data-driven initiatives, dissects datasets, and provides actionable insights to enhance business performance and network efficiency.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.