Top Hybrid Data & Analytics Jobs

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.

Leads data engineering and analytics engineering teams delivering production pipelines, curated data products, semantic models, dashboards, and governed Databricks lakehouse capabilities. Owns platform administration, data quality, observability, security, cost optimization, reliability, and engineering standards. Manages internal and external resources, mentors engineers, drives AI-assisted development, partners with stakeholders, and reports roadmap progress to leadership. Requires strong hands-on Databricks expertise, technical leadership, advanced SQL, data modeling, CI/CD, and up to 5% travel.

Own the architecture and standards of the company’s dbt project, building scalable models across staging, intermediate, and mart layers. Partner with product, design, and engineering teams to evolve data models, validate metrics, and maintain data quality. Develop AI-assisted testing, documentation, and access workflows; collaborate on Segment, Fivetran, and Airflow pipelines; and drive privacy, governance, and warehouse best practices.

Lead data analysis and modeling for Growth and Monetization, focusing on sales trials, product-led sales, pricing, packaging, and monetization. Partner cross-functionally to generate actionable insights, maintain dbt models and ELT pipelines, improve data workflows, evaluate emerging tools, and enable colleagues to use Hex effectively. The role requires strong analytical judgment, SQL and dbt expertise, and an interest in applying AI to data work.

Leads quantitative and qualitative Medicaid policy research from project scoping through dissemination. Oversees data acquisition and analysis, monitors state and federal policy changes, develops briefs and presentations, conducts stakeholder interviews, supervises research staff, manages external collaborators, and translates findings into actionable recommendations for policymakers, providers, advocates, and the public.

Develop global engagement strategies for Crunchyroll’s streaming platform using consumer behavior, audience segmentation, experimentation, and analytics. Analyze viewing journeys, engagement barriers, churn signals, and lifecycle interventions. Build measurement frameworks, define success metrics, evaluate A/B tests, and identify strategies that strengthen viewing habits and retention. Partner with Product, Data Science, Lifecycle, Curation, and Consumer Insights teams to translate complex behavioral data into executive-level recommendations and prioritized initiatives.

Conduct quantitative and qualitative analysis supporting global go-to-market and field services teams. Analyze retention, churn, product adoption, forecasting, territory design, and performance data; build dashboards, reports, queries, and automated data pipelines using Python and SQL. Partner with systems, operations, sales, and executive stakeholders to ensure data quality, prioritize requests, operationalize insights, and drive revenue-impacting decisions.

Develop and improve IP geolocation automation algorithms using large datasets and IP network topology. Query, extract, and transform data with SQL, R, Python, MongoDB, and BigQuery; provide insights that improve data quality; and collaborate with engineers, data scientists, and stakeholders to solve complex problems and lead data science initiatives.