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

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

The Principal Data Analyst will manage pricing and profitability strategy for Workhuman's E-commerce store, leveraging data analysis and cross-functional collaboration to drive decision-making and implement improvements in pricing strategies.

This role focuses on post-sale technical execution, ensuring customer onboarding and deployment, driving customer consumption of products, writing production code, diagnosing issues, and expanding adoption across teams.

The Principal SAP PaPM Configuration Engineer analyzes and configures use cases, supports production users, and mentors team members to enhance SAP PaPM systems within the finance environment.

Serve as a Senior Data Analytics Consultant specializing in Dynatrace and observability to analyze digital experience data, produce dashboards and reports, advise 4–8 enterprise clients, present findings to technical and executive audiences, configure tooling, influence product roadmap, and support client enablement and account growth.

The Associate Principal will translate client needs into analytical specifications, ensure quality control of analytical outputs, and deliver clear insights while overseeing methodologies and clinical context research.

As a Senior Data Engineer, you'll design scalable data models, build automated ETL pipelines, and mentor junior engineers while collaborating across teams.

The Senior Analyst in AML KYC will conduct detailed customer onboarding, monitor client relationships for compliance, and update KYC records while utilizing AI tools for efficiency.

As a Staff Product Analyst, you'll define analytics strategies, build measurement infrastructure, design experiments, and partner with product teams to boost self-serve conversion and product adoption.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.