Top Hybrid Data & Analytics Jobs

Owns go-to-market funnel reporting and analytics from lead generation through pipeline, bookings, and ARR. The role analyzes conversion, pipeline quality, CAC, LTV, marketing ROI, and sales efficiency across segments and channels. It builds dashboards, forecasts, and executive insights while partnering with Marketing, Sales, Revenue Operations, and Finance on attribution, data integrity, budgeting, and investment decisions.

Develop and maintain Java applications, RESTful APIs, backend services, and cloud-ready integrations using AWS. Write optimized SQL, automate tasks with Bash, manage code with Git, troubleshoot production issues, and participate in architecture discussions, code reviews, and continuous improvement. The role requires ownership of end-to-end technical solutions and collaboration across teams.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Own and build January’s foundational data systems, including end-to-end pipelines, data quality standards, legacy migrations, recovery processes, and documentation. Partner with engineers, analysts, operations, and product teams to establish trusted, scalable data infrastructure. Prioritize high-impact improvements, manage stakeholder needs, define ownership boundaries, and use AI to create increasingly self-healing systems.

Build AI agents and data acquisition systems, evaluate dataset quality, define quality KPIs, and improve data processes. Partner with product teams to deploy data into customer-facing products and acquire datasets from third parties, including coordinating vendors and contracts. The role requires organizing multiple projects, structuring analyses, communicating tradeoffs, and using data to support investment decisions and customer outcomes.

Analyze product and healthcare billing data to support product decisions, reporting, experimentation, and claims collection improvements. Build and maintain dashboards, develop scalable datasets, conduct A/B testing, create data visualizations, and communicate actionable insights. Collaborate with product stakeholders, data scientists, and engineers on data modeling, OKRs, data quality, and pipeline reliability for Billing and Payments features.

Lead analytical investigations and build measurement frameworks, dashboards, reporting, and reusable data products for Customer Experience leadership. Partner with data scientists on customer health scoring, forecasting, segmentation, and validation. Use SQL and Python to turn ambiguous business questions into decision-ready insights, document solutions, and operationalize analytics across CX teams.

Supports data pipeline development, data modeling, wrangling, semantic layers, dashboards, and reporting using SQL, Python, AWS, and Snowflake. Collaborates with cross-functional teams to translate business requirements into data solutions and applies data governance, security, and quality practices. This co-op is designed for a currently enrolled university student available January 11 through June 25, 2027, and willing to relocate to Framingham, Massachusetts.

Build and maintain recruiting data pipelines, datasets, integrations, dashboards, automations, and data quality controls. Partner with recruiting, data, security, privacy, engineering, and business teams to improve operational reporting and workflows. Responsibilities include integrating ATS and HR systems, monitoring pipeline stability, supporting writeback processes, designing secure access solutions, documenting sensitive workflows, and adapting data products as recruiting processes evolve.

Own Tomo’s data function, strategy, roadmap, platform health, and cross-functional measurement. Build and lead teams spanning data science, analytics, business intelligence, and data engineering. Design experiments, marketing attribution models, SQL queries, and dbt models while maintaining reliable data infrastructure. Partner with Product, Engineering, and executives to improve instrumentation, translate business questions into insights, and establish scalable data processes.