Top Data & Analytics Jobs

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.

Leads global engagement strategy for Crunchyroll’s streaming platform by analyzing consumer behavior, content performance, engagement KPIs, and lifecycle journeys. Develops strategies to increase viewing frequency, habit formation, retention, and subscriber value. Partners with Product, Lifecycle, Consumer Insights, Data Science, and Analytics teams to prioritize initiatives, design measurement frameworks, evaluate experiments, segment audiences, and translate complex data into executive-level recommendations.

Designs, builds, and supports scalable data pipelines, cloud-native processing systems, data models, APIs, and analytics solutions. The role ensures data quality, observability, testing, governance, CI/CD, and production reliability while partnering with engineering, product, analytics, BI, and business stakeholders. Responsibilities include architecture decisions, troubleshooting, documentation, Agile delivery, incident response, and mentoring engineers.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

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.

Build and extend cloud data warehouse ELT pipelines integrating HR, recruiting, and performance data. Support data governance, retention, lifecycle management, quality assurance, root-cause analysis, and regulatory compliance. Contribute to an AI-powered analytics platform by expanding its semantic data layer while protecting sensitive workforce information. Collaborate with People Operations, Compensation, Talent Acquisition, and Engineering stakeholders to deliver scalable data solutions.

Advise Technology stakeholders on data ownership, governance, risk, and management practices. Lead maturity assessments, strategic roadmaps, operating model improvements, metrics, executive reporting, and capability enhancement initiatives. Partner with senior leaders across Technology, Risk, Controls, and CDAO to improve data quality, metadata, regulatory alignment, and governance effectiveness. Identify opportunities to apply AI, automation, and modern data platforms to scale data management outcomes.

Designs and maintains governed, analytically ready datasets for AML/KYC operations. Builds scalable ETL and transformation processes using SQL and Python, develops metadata and semantic layers for AI and self-service analytics, and implements data quality, reconciliation, and validation frameworks. Partners with operations, data, technology, and AI teams while supporting production data assets, documenting findings, addressing source-system impacts, and ensuring compliance with controls, privacy, and regulatory standards.

Leads the design, development, and operation of production-grade data pipelines and governed data products supporting operational resiliency and recovery-risk analytics. Responsibilities include data modeling, ETL, database optimization, data quality and lineage, cloud and event-driven architectures, automation, testing, and CI/CD. Partners with cybersecurity, technology controls, engineers, architects, analysts, and business stakeholders to translate risk and control requirements into scalable, auditable solutions.

Leads analytics strategy and advisory relationships for strategic publisher partners. Builds performance scorecards, reporting, forecasts, measurement models, financial and partner-economic analyses, and executive-ready narratives. Designs experiments, evaluates incrementality, develops scalable and automated analytics solutions, and shapes the publisher analytics roadmap. Partners with Finance, Business Development, and Strategic Partner Management while mentoring analytics staff and contributing to industry thought leadership.

Leads TFB Sales analytics and compensation execution focused on Total Billed Revenue. Owns quota methodology, analyzes seller activity, validates compensation data, resolves inquiries, develops reporting and decision-support tools, and communicates insights to leadership. Partners with segment owners and senior management on quota and compensation initiatives while supporting operational continuity and team cross-training.

Leads analytics engineering standards and builds governed Gold-layer data models, domain data marts, metric views, dashboards, and self-service analytics on Databricks. Develops SQL and PySpark transformations, data quality tests, documentation, lineage, and access controls. Partners with stakeholders to define enterprise metrics, reviews technical work, mentors engineers, manages data contracts, and improves reporting estates. Applies CI/CD, AI-assisted development, performance optimization, and cost management while leading projects through adoption.

Leads Liberty Mutual’s Global Legal & Compliance Data Office and owns its data and AI strategy, governance, quality, architecture, enablement, and investment roadmap. The role integrates product management, data science, knowledge management, and information services while ensuring regulatory compliance, data readiness, and alignment with enterprise strategy. It drives data maturity, change leadership, cross-functional collaboration, and adoption of data-driven practices across legal and compliance teams.