Top Analytics Engineer Jobs in Boston, MA

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 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.

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 Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

The Enterprise Performance Analytics Engineer builds and maintains data pipelines and models, ensuring data quality and efficient reporting for analytics teams.

As an Analytics Engineer at Axios, you'll design and maintain data products and visualizations, improving decision-making through actionable insights.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, batch and near-real-time processing, data warehouse and data lake structures, data models, query optimization, and data quality monitoring. Support AI workflows, collaborate with architects, analysts, and stakeholders, and maintain documentation for data platforms and pipelines.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Design, build, and maintain scalable, documented data models and the Gold data layer using dbt/Coalesce. Ensure data quality through automated tests, observability, version control, and CI/CD. Optimize SQL and warehouse performance, collaborate with Data Engineering and Analytics, and support semantic layers and BI catalogs to enable trusted, self-service insights for Product, Finance, and GTM teams.

As a Billing & Analytics Software Engineer, you'll design and maintain the billing pipeline, integrate billing systems, and develop customer-facing analytics dashboards to support revenue recognition and operational observability.

As a Data Analytics Engineer I, you'll bridge engineering and analytics, manage data pipelines, collaborate with stakeholders, and utilize modern open-source tech for data transformation and integration in structured finance.

The Senior Finance Analytics Engineer designs dbt models, collaborates on metrics with finance teams, and enhances data for AI consumption while maintaining SOX compliance.

Design and maintain scalable data models and pipelines for analytics. Collaborate with teams to ensure reliable data supporting business insights.

The RevOps Analytics Engineer is responsible for maintaining data infrastructure, building reliable data systems, and supporting revenue analytics through effective data modeling and ETL/ELT processes.

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 analytics engineering: design scalable dimensional models, build modular tested transformations, replace brittle pipelines with reusable patterns, drive semantic layers and self-service analytics, partner on data architecture, and provide cross-functional technical leadership and governance.

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.

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.

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.

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.

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.

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.