Top Analytics Engineer Jobs in Boston, MA

Build and maintain scalable dbt models in Snowflake, implement data quality tests and documentation, collaborate with Analytics and Data Engineering on ingestion and warehouse optimization, support data governance and apply software engineering best practices to analytics code.

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 own the Snowflake/dbt data infrastructure for WHOOP's financial planning model: standardize financial actuals, develop Snowflake-native functions and stored procedures, translate FP&A modeling into version-controlled SQL/dbt models, integrate source systems, implement data quality tests, support Sigma front-end, document lineage, and apply software engineering best practices while implementing AI-augmented forecasting and automation.

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.

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.

Lead design and implementation of Affirm's Lakehouse Platform using Iceberg, Spark, Snowflake, and cloud storage. Drive platform roadmap, governance, access controls, performance and cost optimization, analytics engineering best practices, observability, and on-call readiness. Partner cross-functionally, mentor engineers, and build scalable, secure, self-service data infrastructure and semantic/agentic data tools.

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

Build, operate, and extend data platform and application-layer services enabling secure, self-service data applications at scale. Implement provisioning, CI/CD, access control, integrations, observability, automation, and incident response while contributing to semantic layer and AI-ready tooling roadmaps.

Lead architecture and implementation of a scalable data lakehouse and streaming platform. Define technical strategy, build resilient systems, optimize performance, mentor engineers, and partner across teams to enable analytics and ML use cases using modern data platform technologies.

Develop and improve inline metrology flows and failure-analysis methods for photonic quantum hardware. Build and analyze SPC charts, perform RCA, design experiments, coordinate with software and external partners, purchase and qualify metrology tools, and apply statistical analysis to improve yield and wafer performance.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

Partner with stakeholders to define metrics and deliver dashboards and insights. Build and optimize data models in Amazon Redshift, design and maintain scalable ETL pipelines using Python and Apache Airflow, monitor data quality and pipeline performance, and participate in code reviews and engineering best practices to support analytics and reporting needs.

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.

Build and maintain scalable Python-based data pipelines and backend services for analytics and ML workflows. Design software systems, support analytics/model training and deployment platforms, implement CI/CD and IaC, manage AWS/on-prem environments, monitor and improve pipeline reliability, and evaluate new AI technologies to increase platform efficiency.

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 design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.

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.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.