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

Lead the development and implementation of data architecture strategies, mentor a team to deliver solutions, and collaborate with stakeholders to enhance resources while ensuring project success and quality outcomes.

The Associate Principal will translate client needs into analytical specifications, ensure quality control of analytical outputs, and deliver clear insights while overseeing methodologies and clinical context research.

The Director of Biostatistics leads a team in clinical research, developing statistical plans, collaborating across departments, and ensuring regulatory compliance.

Lead end-to-end analytics for Veterinary Diet, Science Diet, and Health & Wellness businesses. Build self-service dashboards, define KPIs, run deep-dive analyses, partner with technical and category teams, and deliver insights that improve customer experience and financial P&L.

Lead Data Product Management for G&A functions, shaping strategy, coordinating execution, and establishing practices within Finance, HR, and IT sectors. Engage stakeholders, ensure data quality and governance, and manage team growth while directly overseeing product delivery.

As a Senior Data Engineer, you'll design scalable data models, build automated ETL pipelines, and mentor junior engineers while collaborating across teams.

As a Lead Data Engineer, you will collaborate in Agile teams to design and implement financial data solutions, utilizing extensive programming and cloud technologies.

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 role leads Clinical Data & Information Sciences in drug development, ensuring data integrity, compliance, and continuous improvement while collaborating with cross-functional teams to enhance business processes and experience.

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.