Top Data Analyst Jobs

The role involves interpreting, validating, and structuring private equity investment data, collaborating with teams, and communicating with investment managers, while improving internal processes and data standards.

The Senior Data Analyst will develop advanced Power BI dashboards, perform complex analyses using SQL and Python, mentor junior analysts, and ensure data quality governance.

The Senior Data Analyst will own analytics projects, design data models, build dashboards, and deliver insights using SQL and various BI tools.

The Data Support Analyst will provide support for project tools and data, manage project financials, and prepare management reports.

The Sr Data Systems Analyst will conduct data analysis, develop business intelligence solutions, and lead projects while mentoring the team.

Perform end-to-end marketing analytics: define and maintain KPIs, run SQL analyses, build Tableau dashboards, apply diagnostic and predictive techniques, and present actionable insights to marketing and product stakeholders to improve campaign performance and operational efficiency.

The Data Modeler or Analyst role involves modeling data using Oracle and Erwin, focusing on data warehousing with fact and dimension tables. Requires 5-7 years of relevant experience.

The Sr. Data Analyst will utilize industry knowledge to analyze data, develop solutions, and support customers globally, focusing on delivering high-quality IT services.

Seeking a Sr. Data Analyst with 5+ years in data analysis, strong SQL skills, and healthcare industry experience. Responsibilities include data mapping and analysis.

Seeking a Sr. Data Analyst with 5+ years experience in data analysis, particularly in the healthcare sector, with strong SQL skills.

The Data Modeler/Analyst will collect, organize, and interpret data. Responsibilities include data profiling, modeling, visualization, and documenting mappings, alongside ensuring data governance compliance.

Perform hands-on data analysis, profiling, reconciliation, and validation using Databricks and SQL. Lead UAT design and execution, coordinate testing with stakeholders, validate dashboards and workflows, document defects and recommendations, and support Agile delivery and release readiness using tools like Jira and Confluence.

Support definition, validation, testing, and stabilization of mission-critical data integrations and interfaces across source systems, staging, transformation layers, and reporting platforms. Perform source-to-target validation, reconciliations, interface testing, cutover readiness, production stabilization, issue triage, and documentation to ensure accurate federal financial reporting.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.

Partner with Sales, RevOps, Marketing, and Customer Success to connect the full revenue funnel. Produce GTM insights using advanced analytics, SQL, and BI tools; communicate findings to stakeholders; standardize Data team processes; mentor analysts and lead data-driven initiatives to drive business outcomes.

Analyze player behavior and drive product innovation through data-driven insights. Collaborate with teams to design and evaluate experiments, maintaining data quality and delivering meaningful insights.

The Senior Data Analyst will manage client-facing analytics, build dashboards, partner with Customer Success, and leverage AI tools for reporting.

The Senior Data Analyst will manage data quality and governance for regulatory compliance, build data marts, and optimize data models for performance and scalability. This role includes supporting regulatory exams, establishing monitoring for data feeds, and translating regulatory requirements into data flows.

Own the conversion of raw alternative datasets into scalable, AI-ready data products. Define methodologies, metric definitions, and guardrails for AI-driven insights. Partner with engineering, product, and customers to expand use cases, ensure data quality, validate changes, and manage incidents to deliver reliable, explainable data products at scale.

Lead analytics and evaluation for search and retrieval systems: define measurement strategies, run experiments, analyze ranking and user behavior, build dashboards, ensure data quality, and partner with product, data science, and engineering to improve relevance and retrieval performance.

Design and maintain Looker/LookML data models and dashboards, perform end-to-end SQL analysis, ensure data quality with Data Engineering, build predictive models, and translate findings into actionable recommendations for cross-functional stakeholders.

The Data Operations Analyst will manage projects supporting AI model development, collaborating with teams to ensure project success and deliver high-quality datasets.

Design, build, and maintain Workday integrations, data architectures, and reporting to support workforce planning and finance alignment. Deliver analytics, self-service reporting, and AI-enabled insights; partner with HR, Finance, and IT to enable accurate headcount forecasting, labor cost analysis, and automation of people-data processes.

Build scalable, production-ready data solutions and automations. Perform ad-hoc and deep-dive analyses using Python, PySpark, and SQL. Collaborate cross-functionally, use Databricks and Tableau to generate insights and optimize client marketing performance.

Build scalable, automated data solutions and perform ad-hoc quantitative analyses to identify and forecast trends. Collaborate cross-functionally to optimize client marketing performance using Python, PySpark, and SQL, and produce insights and dashboards with Databricks and Tableau.