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

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Conduct quantitative research using surveys, experiments, and advanced statistical methods for healthcare and life sciences client engagements. Analyze data, develop insights and visualizations, support rigorous research design, and translate findings into actionable recommendations. Collaborate with consulting teams and global delivery groups while ensuring research quality and communicating complex concepts to technical and non-technical audiences.

Leads supply chain master data management and data governance strategy, policies, operating models, ownership frameworks, data-quality monitoring, metadata, lineage, catalogs, and remediation. Partners with cross-functional stakeholders in a regulated biotechnology environment, oversees MDM execution resources, assesses project impacts, manages governance artifacts, and improves data integrity, privacy, compliance, accessibility, and usability.

Own the lab data platform from instruments, ELN/LIMS, and bench workflows through BigQuery, Kernel, and KernelAI. Build ingestion pipelines, schemas, APIs, quality checks, lineage, versioning, ML-ready datasets, reporting tools, and dashboards. Partner closely with biotech scientists and computational teams to standardize experimental data and support biological modeling and design.

Build and maintain Power BI or Tableau dashboards, write SQL against Snowflake, define KPIs and data models, validate AI-generated analytics, and improve report performance. Partner with finance, sales, operations, and data engineering stakeholders to scope reporting needs, manage dashboard projects, identify data quality issues, and develop semantic-layer definitions. Train business users, create documentation, support self-service analytics, gather feedback, and track dashboard adoption.

Owns the architecture and implementation of Absentia Labs’ biomedical data platform. Designs schema-driven models, scalable batch and streaming pipelines, and cloud-native infrastructure supporting machine learning workflows. Establishes data quality, validation, provenance, lineage, and versioning practices while partnering with scientists, ML engineers, and product leads. Provides architectural leadership, mentorship, and guidance on performance, cost, scalability, and correctness.

Build and lead enterprise data capabilities for a global biotechnology company. Design data architecture, pipelines, APIs, integrations, governance, master data management, analytics, and AI foundations across commercial, R&D, technical operations, quality, and corporate functions. Lead a high-performing team, deliver self-service reporting, partner with technology and business stakeholders, manage vendors, and ensure data security, quality, privacy, and regulatory compliance.

Lead the architecture and development of UKG’s Unified FinOps Data Platform. Build scalable cloud-native data products and pipelines for cloud billing, usage, cost allocation, forecasting, optimization, reporting, and unit economics across AWS and GCP. Establish governance, data quality, lineage, observability, CI/CD, and infrastructure-as-code standards. Support AI-enabled forecasting and optimization, collaborate with Finance and Engineering stakeholders, mentor engineers, and drive adoption of self-service reporting.

Conduct media mix modeling, experimentation, incrementality analysis, forecasting, and marketing performance reporting. Build scalable analytical tools, automated workflows, Tableau dashboards, and statistical resources using SQL, Python, and BI platforms. Partner with Marketing, Data Science, Data Engineering, Finance, and Sales teams to improve measurement capabilities, operationalize data structures, and translate complex findings into actionable business recommendations. Maintain analytical standards, documentation, and quality controls.

Build and maintain large-scale data pipelines and analytics platforms for EDA datacenter observability, telemetry, reliability analysis, and capacity forecasting. Develop batch and streaming workflows, data models, quality frameworks, monitoring, and retention strategies using metrics, logs, traces, and hardware health data. Partner with observability engineers, data scientists, analysts, and infrastructure teams to deliver scalable, reliable, discoverable datasets for analytical and predictive use cases.