Top Analytics Engineer Jobs in Boston, MA

The Analytics Engineer will build and optimize data transformation models, assist with BI tools, support stakeholders, document processes, and implement AI tools, contributing to a data platform under mentorship.

The Staff Analytics Engineer leads business intelligence initiatives, develops a governed semantic layer, and advocates for a data-driven culture while leveraging AI tools for analytics.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

Build and maintain production-grade data models and pipelines for the Compliance Data Mart, implement data quality frameworks and monitoring, partner with engineering to resolve data gaps, support regulatory exams and audits, and automate manual workflows into scalable pipelines and self-serve tooling.

The Staff Analytics Engineer will own the Financial Subledger Data Platform, build dbt models, implement data quality controls, and mentor a junior engineer, ensuring high operational reliability and cross-functional collaboration.

The Data Engineer will design and build data solutions, develop ELT jobs, ensure data quality, and collaborate with technical teams to align data strategies with business goals.

Owner of the semantic layer: build and maintain core data models and metrics, run dbt orchestration with CI/CD and testing, implement reverse ETL pipelines, and enforce PHI access controls to keep data HIPAA-compliant.

The Staff Analytics Engineer will architect and manage CodeRabbit's BigQuery warehouse, develop revenue models, and ensure data governance while collaborating with various teams to enhance business intelligence.

Lead design, implementation, and optimization of data pipelines, models, reports and Power BI/SSMS dashboards for public safety clients. Configure analytics databases, train agency users, produce instructional materials, mentor junior engineers, and serve as technical SME and escalation point for multi-agency analytics implementations.

The Senior Analytics Engineer will develop data infrastructure, optimize data systems, ensure data quality, and collaborate on scalable data models and pipelines, focusing on analytics support.

Lead architecture, build, and operate customer-facing analytics platform: design ELT and metric governance, implement low-latency serving (OLAP), ensure reliability/performance, mentor engineers, partner with product, and use AI coding tools to accelerate development.

Designs, builds, and operates ELT data pipelines and analytical datasets for Platform Analytics. Curates and models source data into conformed datasets, ensures performance, reliability, and cost efficiency, contributes to target-state architecture, mentors junior engineers, and uses AI coding tools to accelerate development and delivery.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

The GTM Analytics Engineer will enhance data usage across the revenue organization by refining models, designing workflows, automating processes, and partnering with teams to improve GTM strategies.

The role focuses on implementing cost optimization solutions for cloud infrastructure through automation and collaboration across teams, enhancing FinOps practices.

Design, build and maintain scalable, auditable analytics-ready data models (primarily in dbt) for PBM and care navigation. Implement data quality checks, CI/CD, documentation, pipeline orchestration, and partner with stakeholders to enable self-service analytics and AI-enabled workflows.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

As an Analytics Engineer, transform raw data into metrics, manage data pipelines, ensure data integrity, and collaborate with teams for analytics solutions.

The Senior Data Analytics Engineer will develop data pipelines, support data-driven decision-making, collaborate with stakeholders, and improve data quality and processes.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.