Top Hybrid Data & Analytics Jobs

Build data pipelines and relational data models using Python and SQL; clean operational data, support scenario analysis, and create dashboards for PMO leadership. Explore LLMs and machine learning for text analysis and data-quality automation. Gain production experience with testing, documentation, maintainable code, and data-driven decision support in a trading environment.

Lead Notion’s Data Foundations team by setting multi-quarter platform strategy, developing engineers and technical leaders, and guiding complex initiatives across data lakes, streaming, distributed compute, governance, and reliability. Drive enterprise-ready capabilities including data residency, access controls, lifecycle management, and key management. Improve Kafka, Debezium, S3/Iceberg, Spark, EMR, and Athena scalability, reliability, usability, and cost efficiency while partnering across engineering, AI, search, infrastructure, and security teams.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Conduct quantitative research to design, implement, and deploy mathematical trading models. Build custom tools for exploratory analysis, apply rigorous quantitative techniques to solve complex problems, and collaborate with traders and developers to productionize models while maintaining best practices.

Provides portfolio-level data management, reporting, dashboards, and analysis for strategic initiatives. Supports executive decision-making by monitoring milestones, forecasts, risks, data integrity, schedules, and operational efficiency. Partners with portfolio leads, business owners, and stakeholders to resolve data issues, improve processes, prepare meetings, and facilitate prioritization and sequencing discussions.

Develops Tableau dashboards, writes optimized SQL, and supports analytics initiatives using large datasets. The intern collaborates with business and technical teams to gather requirements, troubleshoot data issues, and improve access to internal data. Responsibilities include contributing to data warehouse design, maintaining ETL processes, coding, testing, debugging, documentation, and using AI-assisted development tools such as Claude Code. The role operates in a regulated, agile environment and supports data-driven decision-making.

Build data and semantic models, documentation, context, and evaluations that improve conversational analytics powered by LLMs. Embed with stakeholder teams to understand analyst workflows and business needs, then develop self-service tooling and standardized analytic frameworks. Train non-data stakeholders on effective data and LLM usage, create reusable training materials, maintain evaluation processes, and track conversational analytics accuracy. Requires strong SQL, dbt modeling, BI, communication, teaching, problem-solving, and project ownership skills.

Develop revenue, yield, and business intelligence analyses; build scalable SQL- and Python-based solutions; create and maintain Tableau dashboards; conduct statistical analyses, discrepancy checks, and A/B testing; identify business opportunities; and communicate actionable insights to stakeholders across sales, account management, publisher, DSP, agency, and executive teams.

Conduct research and analytics for Finance and Travel audiences using first-party, syndicated, quantitative, qualitative, and secondary data. Synthesize large datasets into insights supporting advertising partnerships, campaigns, RFPs, and ad hoc requests. Advise Marketing, Editorial, and Sales teams on audience growth and brand strategy, design consumer research studies, create data-driven stories, and present findings to technical and non-technical audiences.

Lead data engineering initiatives supporting analytics, machine learning, and business intelligence teams. Design complex data warehouse and data lake models, manage large-scale SQL and Python ETL/ELT pipelines, maintain data quality and lifecycle standards, and translate strategic priorities into actionable projects. Provide technical leadership, mentorship, architectural guidance, project planning, and cross-functional collaboration with product, infrastructure, and architecture teams.