Top Data & Analytics Jobs

Lead a small senior data team to deliver attribution, incrementality, LTV/CAC, segmentation, forecasting, and experimentation. Own measurement of cross-product flywheels, guide data platform investments, embed experimentation, and partner with executives to influence capital allocation and GTM decisions while doing hands-on analysis.

Own and maintain dbt models, ELT pipelines, and warehouse architecture (Snowflake/Redshift/BigQuery). Ensure data quality, testing, documentation, lineage, and metric governance. Partner with Finance, Operations, and Engineering to deliver authoritative KPIs, support executive reporting, and drive analytics engineering standards and roadmap.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

The Data QA Engineer is responsible for ensuring data quality through automated testing frameworks, SQL queries, and collaboration with analytics teams.

The role involves building data platforms and pipelines, integrating AI solutions, and collaborating with product owners to address complex client issues.

Responsible for driving the technical roadmap, developing prediction models, collaborating across teams, and mentoring new talent while delivering data-driven insights.

Lead customer-facing analytics: build dashboards, reports, and POCs that demonstrate ROI and utilization trends for healthcare provider data. Collaborate with data science and engineering on advanced models, assess and integrate new data sources, ensure data quality, and support presales and executive-facing storytelling.

The Data Analyst at PitchBook provides high-quality data analysis for private market research, utilizing Excel and SQL to deliver insights and support clients. Responsibilities include managing data requests, collaborating with research analysts, and contributing to content development for various media.

Lead design and build scalable data pipelines, data models, and ETL/ELT workflows. Mentor data engineers, set coding standards, improve data architecture and tooling, partner with Product and Platform teams, and deliver monitoring, data quality, and automation to support Data Operations.

Serve as a strategic analytics partner to Product, Engineering, and Business leaders to define and measure the customer journey, identify funnel optimization opportunities, run deep-dive analyses and A/B tests, build measurement frameworks and dashboards, partner with Data Engineering to establish scalable data pipelines and event tracking, translate insights into actionable recommendations, and mentor junior analysts to influence product strategy and business outcomes.

Analyze digital marketing campaigns and leverage data to inform business decisions, drive optimizations, and improve performance metrics.

The Senior Data Analyst will analyze digital marketing campaigns, leverage data sources for business decisions, and communicate insights effectively to improve performance and support Media Partners.

Own marketing channel performance tracking, reporting, and analysis. Design and analyze experiments (A/B, incrementality), build dashboards in Looker/Tableau, support LTV and forecasting models, identify measurement gaps, and translate analysis into clear recommendations for Growth, Marketing, and Finance stakeholders.

Design and deliver executive-ready Power BI reports, dashboards and agentic experiences. Build semantic models and self-service analytics, apply DAX, Power Query and Fabric notebooks, perform data exploration with SQL/KQL, and drive AI-enabled automation. Collaborate with data engineering to influence ingestion and transformation, translate business strategy into data solutions, and ensure performance, reliability, and scalability of reporting assets.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Design, implement, and optimize marketplace risk strategies to mitigate buyer/seller/account abuse and payment fraud. Use large-scale analytics, experimentation, and partnership with Data Science and engineering to operationalize models, monitor KPIs, identify fraud trends, and balance fraud prevention with user experience. Mentor analysts and lead cross-functional initiatives to protect platform integrity.