Top Analytics Engineer Jobs in Boston, MA

Own end-to-end data pipelines and production-grade dbt models (Snowflake) for Sales and Product analytics. Build governed data marts, dimensional models, and reverse ETL workflows. Improve data quality, testing, monitoring, and documentation. Partner with GTM, Product, and BI stakeholders, mentor peers, and contribute to platform tooling (Airflow, Terraform, AWS) to enable reliable, scalable analytics.

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, refactor, and operate a production RWD delivery pipeline using dbt, PySpark, Snowflake, and AWS. Own delivery endpoint configs as code, enforce HIPAA de-identification, manage monthly deliveries, drive data quality metrics and investigations, support channel partners, and collaborate with platform engineering using CI/CD, Argo orchestration, and observability tools.

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.

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.

Design and maintain dbt data models, optimize pipelines with data engineers, translate business requirements into technical specs, enforce code quality and testing, and document a company-wide data dictionary to support reliable reporting and dashboards.

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 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.

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.

Design and own end-to-end data models and dbt transformations on BigQuery, maintain the semantic layer in Lightdash, govern metrics, enable AI-assisted analytics workflows, partner with stakeholders to deliver trusted reporting, and lead data modeling standards, testing, and documentation.

Own and build scalable analytics models, metric definitions, and dashboards (customer-facing and internal). Partner with engineering to design data flows and instrumentation (BigQuery, S3, PostHog). Translate stakeholder needs into analytics plans, triage requests, and surface insights via Looker/Observable. Set analytics stack direction and ensure data quality.

Design, build, and maintain cloud and on-prem data warehouses, data pipelines, and ETL/ELT workflows (Snowflake, SQL Server, Airflow, DBT, Python). Ensure data quality, performance, CI/CD, and support analytics-ready datasets for reporting and BI while collaborating with stakeholders and following Agile practices.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

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

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Design, build, and maintain scalable ELT pipelines and analytical data models. Ensure data quality, validation, and documentation. Collaborate with analysts, data scientists, and engineers to deliver reliable datasets and tooling for reporting and decision-making. Monitor, troubleshoot, and enforce data governance and security; finance domain experience is a plus.

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

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.

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

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.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

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.

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.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.