Top Hybrid Data & Analytics Jobs

Develop market intelligence, forecasting, statistical models, scenario analyses, and automated reporting for supply chain decisions. Integrate internal and external datasets using Python, R, SQL, and other tools; build dashboards; support data governance; and translate analytical findings into recommendations for procurement, planning, and executive stakeholders.

The Data Analyst supports Product and Partner Management leadership by aligning priorities, facilitating cross-functional collaboration, sharing knowledge, and analyzing business information. The role requires querying databases, using Excel and potentially business intelligence tools, evaluating risks and opportunities, supporting requirements gathering, leading meetings, and making informed business decisions while managing multiple priorities.

Lead product-focused analytics for customer-facing interfaces: define and maintain interface metrics, measure design system adoption, run large-scale A/B tests, optimize localization, partner with Product/Design/Engineering, and enable data-driven decision making across teams.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Supports cross-channel marketing attribution and influence modeling by documenting data sources, defining business rules, mapping and normalizing interaction data, testing reporting prototypes, and recommending implementation steps. The intern also assists with AWS marketing data organization, segmentation research, data hygiene, process documentation, and stakeholder communications. The role requires analytical thinking, thorough documentation, clear communication, and collaboration across marketing and technical teams.

Collects, processes, and analyzes complex datasets to produce actionable insights, automated reports, dashboards, and visualizations. Partners with cross-functional teams to ensure data accuracy, integrity, and effective decision-making. Performs statistical analysis, validates data, improves reporting processes, and coaches stakeholders on data consumption. Preferred experience includes applying AI tools, developing AI-enabled solutions, translating business needs into AI use cases, and following responsible AI practices.

Supports enterprise ESG data collection, validation, analysis, reporting, disclosures, assurance, and stakeholder requests. Develops dashboards and executive summaries, manages reporting systems and calendars, documents controls and methodologies, resolves data quality issues, coordinates internal and external assurance activities, researches sustainability standards, and contributes to materiality, scenario, and strategic ESG analyses.

Build and scale data pipelines, APIs, integrations, and data models supporting CRM personalization, segmentation, audience activation, and campaign measurement. Ensure pipeline reliability through monitoring, alerting, and logging; automate workflows; document systems; participate in architecture and code reviews; and mentor junior engineers. Collaborate with data engineering, data science, and CRM teams to improve personalized email, push, and SMS engagement for millions of users.

Leads marketing media measurement strategy across Social, TV, Influencer, and Content Marketing. Owns Marketing Mix Modeling, incrementality testing, causal inference, attribution, experimentation, and media effectiveness analysis. Uses SQL and statistical programming to evaluate customer and media data, develop investment recommendations, and communicate findings to executives. Partners cross-functionally with Marketing, Finance, Data Science, Engineering, Product, and agencies while leading analytics talent and remaining hands-on.

Designs and maintains scalable data pipelines and platform architecture using Python, dbt, Airflow, and Snowflake. Responsibilities include data modeling, ELT development, quality and observability, production debugging, source-data validation, performance optimization, and Tableau reporting. The role leads ad hoc analysis, collaborates with engineering, marketing, product, and leadership teams, makes architectural decisions, and mentors colleagues through code reviews and knowledge sharing.