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

Lead the development of scalable data platforms and analytics systems, overseeing teams in building data pipelines, advanced algorithms, and dashboards for data-driven decision making.

The Business Systems Analyst defines business and systems objectives, analyzes needs, leads projects, and ensures alignment with strategies in investment management technology.

The Senior Revenue Operations Analyst will support go-to-market teams with insights and reporting, optimize processes, and analyze revenue trends to drive operational efficiency and revenue growth.

The Business Analytics Manager will partner with Lifecycle Marketing to enhance member communications, drive engagement, and inform strategic decisions using data analysis and insights.

As a Lead Analyst, you will optimize trading analytics by assessing exchange performance, developing analytical frameworks, and collaborating with teams to enhance trading strategies and customer experience.

Design the architecture for a healthcare data ecosystem, ensuring integration of structured and unstructured data for AI use, while maintaining data quality and governance.

The Senior Analytics Engineer will own data pipelines and models for Sales and Product analytics, ensuring data quality and governance. They will collaborate with cross-functional teams to facilitate data-driven decisions and enhance productivity through scalable data solutions.

Lead and grow an analytics engineering team to deliver scalable data models and reporting for Product, Marketing, Finance, and other stakeholders. Drive BI platform vision, collaborate with Product and Data Governance, prioritize work, implement agile practices, and ensure high data quality and performant transformations.

The Sr. Analytics Engineer at Philo is responsible for designing data models, governing business metrics, collaborating with stakeholders, and ensuring data quality and documentation for analytics purposes.

The Senior Data Engineer will design and optimize large-scale batch and streaming data pipelines using Scala and Spark, oversee the data engineering lifecycle, and engage in technical discussions to improve system efficiency.