Top Hybrid Data & Analytics Jobs

Lead strategic insights and ad measurement projects, analyze complex datasets, craft narratives connecting brand lift to business outcomes, partner with clients and cross-functional teams, and drive process improvements and customer-facing recommendations.

Lead analytics for Udemy Japan partnerships: build and maintain KPIs and dashboards, perform in-depth business and partner analyses, support forecasting and partner-facing events, and provide data-driven insights to sales, leadership, and global partner operations to drive regional revenue growth.

Entry-level Data Engineer responsible for building and maintaining cloud-based data assets and pipelines, improving data quality and automation, investigating data integrity issues, and collaborating with architects, analysts, and stakeholders. Uses SQL and Python, participates in Agile practices, leverages AI-powered developer tools for spec-driven development, and follows responsible AI and data privacy practices.

Lead the implementation of evaluation frameworks for AI systems and clinical assessments, translate clinical evaluations into rubrics, and supervise a team of analysts and engineers.

As a Data Analytics Engineer, you'll build and manage data models in Snowflake, structure datasets for optimal AI performance, and own data transformation while ensuring quality and accessibility for all teams.

The Senior Analytics Engineer transforms curated data into analytical assets, develops BI-ready models, and collaborates with data stakeholders to deliver analytics solutions.

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.

The VP, Global Head of Statistics leads statistical strategy for vaccine clinical research, providing statistical analysis, overseeing a large team, and influencing cross-functional research efforts.

The Data Governance Engineer develops and implements data governance frameworks, ensuring data integrity and quality, and collaborating with multiple teams to support compliance and enhance data reliability through automation and tooling.

Collect, process, and analyze complex datasets; build automated reporting, dashboards, and visualizations; perform statistical analyses; validate data quality; partner with stakeholders and engineering teams to deliver actionable insights and improve reporting processes.