Top Data & Analytics Jobs

Leads consumer and market insights for Yahoo Ads, partnering with sales and go-to-market teams to develop data-driven narratives, audience profiles, vertical trends, case studies, and client materials. Uses Yahoo first-party data, syndicated research, quantitative and qualitative methods, and generative AI tools to accelerate research. Builds validated analytical templates and self-service resources, collaborates on reporting pipelines and dashboards, and translates complex findings into executive-ready presentations.

Analyze operational data to identify trends and correlations, create dashboards and reports, wrangle data from relational and non-relational sources, and apply statistical techniques to generate actionable business insights. The role involves stakeholder engagement, documentation, and improving data literacy through tools including SQL, Power BI, Excel, Python, and Databricks.

Collects, analyzes, and interprets Environmental Health and Safety data to identify trends and support compliance and risk management. Develops dashboards and reports, ensures data accuracy and integrity, provides process improvement recommendations, supports EHS initiatives, and collaborates with IT and engineering teams on data ecosystems.

Two-year early talent program developing data engineering professionals. Responsibilities include building and maintaining scalable data pipelines, integrating and transforming data, developing data models, implementing quality checks, and supporting data governance. The role uses SQL, Python, cloud platforms, and modern data engineering tools while collaborating with engineers, analysts, and business partners on production data solutions.

Leads the Analytics Systems Team of 6–10 engineers, overseeing team development, engineering quality, project delivery, product direction, roadmap prioritization, and strategic analytics infrastructure. The role guides development of data pipelines, analytics tooling, feature stores, and foundation-model integrations supporting clinical trials, diagnostics, biomarker discovery, and regulated Life Sciences workflows.

Own marketing analytics, reporting, dashboards, metric definitions, and customer attributes. Connect Google Analytics, HubSpot, and Salesforce data; support segmentation, campaign measurement, funnel reporting, and data-quality improvements. Partner with Marketing, Data, Revenue Technology, Product, and Engineering to translate business needs into reliable reporting and actionable insights.

Collects, cleans, integrates, and analyzes business data; develops Power BI dashboards, reports, models, pipelines, and data quality controls. Translates business needs into analytical solutions, identifies trends and recommendations, validates results, supports migrations and modernization, and explores AI and machine learning use cases. Partners with technical and nontechnical stakeholders while documenting data lineage, assumptions, governance, privacy, and responsible AI practices.

Optimize onsite search, navigation, taxonomy, and digital merchandising to improve product discovery and conversion. Analyze large datasets and KPIs, build dashboards, run A/B tests, and implement search relevance changes (boosts, synonyms, redirects) in partnership with merchandising, product, UX, IT, and vendor partners to increase ecommerce revenue and engagement.

The Data Engineer Intern will onboard and model datasets on cloud data platforms, build reliable ETL/ELT pipelines, and deliver high-quality data layers for analytics, reporting, and AI/ML workloads. Responsibilities include working with Databricks or Spark, cloud platforms, relational and NoSQL databases, data quality frameworks, version-controlled data models, Git, CI/CD, and modern development workflows. The intern will collaborate with AI, data, and finance professionals in a team environment.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.