Top Data Analyst Jobs

Analyzes advertising revenue, inventory utilization, sell-through rates, pricing, and business trends. Maintains Excel and Power BI reports and dashboards, organizes data from multiple sources, investigates discrepancies, and creates clear visualizations. Supports process automation using Power Query, Excel data models, and Access. Collaborates with analytical leads and cross-functional partners to communicate findings, improve reporting accuracy, and support business decisions.

Lead enterprise reporting and analytics initiatives across healthcare operations. Develop dashboards, scorecards, KPIs, data standards, analytical models, and executive insights using healthcare claims data. Reconcile data sources, identify anomalies and performance drivers, automate reporting, and translate business needs into scalable solutions. Serve as a reporting intelligence subject matter expert, collaborate with cross-functional stakeholders, influence strategic decisions, and mentor junior analysts.

Analyze complex data, build self-service data solutions, develop business intelligence dashboards, and deliver strategic insights. Manage data quality, metadata, lineage, access governance, and monitoring. Partner with business and technology teams while using Python, R, Spark, SQL, AWS, and visualization tools. The role requires substantial data analytics, scripting, querying, business intelligence, data governance, and process improvement experience.

Supports Finance and cross-functional teams by building data pipelines, improving data quality and governance, automating reporting, developing Power BI dashboards, applying AI and analytics, and contributing to financial models, forecasts, reconciliations, and executive insights.

Analyzes complex data to develop business intelligence tools, metrics, dashboards, and actionable insights. Builds data solutions and self-service capabilities using Python, R, Spark, SQL, AWS, Snowflake, OneLake, and Databricks. Partners with stakeholders on strategy and operational improvements while supporting data quality, metadata, lineage, access governance, and monitoring. The role also advances analytics innovation and automated data management across Bank Tech.

Leads advanced analytics, reporting, scalable data models, dashboards, and data pipelines supporting Business and Marketing teams. Partners with senior stakeholders and AI Engineering to implement AI and machine learning workflows, predictive insights, and automation. Develops documentation, drives analytics engineering best practices, and mentors team members. Requires strong SQL, Python, APIs, data modeling, stakeholder communication, project leadership, and blockchain expertise.

Performs advanced data analytics across innovation, business intelligence, and data management. Builds data solutions, self-service tools, dashboards, metrics, and data-quality monitoring capabilities; analyzes complex structured and unstructured data; partners with business and technology teams; and supports data governance, security, metadata, lineage, and access management.

Lead complex data analysis across Liftoff’s supply-side business, using optimized SQL on large-scale datasets and BI tools to build dashboards, reporting pipelines, and actionable insights. The role conducts root-cause analysis, evaluates experiments, translates technical developments into business impact, leads cross-functional initiatives, and mentors analysts. It partners with Engineering, Product, Data Science, Finance, Commercial teams, and executive leadership to influence roadmap and operational decisions.

Maintains and analyzes investment data for clients by organizing raw data, building data systems and pipelines, integrating multiple sources, and improving data quality. Develops analytical tools, performs data mining, creates visualizations and reports, and collaborates with architects, product teams, clients, and project managers to meet business requirements.

Build and automate client marketing reports and dashboards using Power BI, SQL, Excel, and Google Sheets. Analyze performance data from digital advertising and analytics platforms, interpret metrics such as CTR, CVR, ROAS, and attribution, identify anomalies, and provide actionable insights. The role also improves reporting processes, documents workflows, and supports data integration from advertising platforms, CRMs, and other sources.

Analyzes ticket sales, marketplace supply, event characteristics, and external signals to support pricing, inventory, and business decisions. Builds dashboards, reports, predictive models, and analytical tools using Looker, Python, Excel, SQL, and Snowflake. Automates recurring analyses, resolves data-quality issues, partners with stakeholders, and presents findings. The role progresses toward independently leading small analytical projects from problem definition through recommendations.

Manages platform data and develops reporting and analytics on usage, adoption, capacity, performance, risks, and trends. Partners with technology, analytics, and business teams to support enterprise data platforms through onboarding, training, and user support. Communicates governance updates and recommendations to leadership, promotes compliance with data governance and risk controls, and advises users on data manipulation and statistical analysis.

Leads enterprise-wide validation of analytical tools, including planning validation projects, assessing methodology quality and tool risk, researching best practices, and communicating findings through reports and presentations. Partners cross-functionally to influence business strategy, handles concurrent projects, develops solutions to business problems, and coaches junior staff. The role requires substantial data analysis, statistical or econometric modeling, large-scale data manipulation, open-source technology experience, and people management.

Analyze complex data to support business decisions, build self-service data solutions, develop dashboards and metrics, and improve data quality and governance. The role uses Python, R, Spark, SQL, AWS, business intelligence tools, and data management practices while partnering with business and technology teams on analytics and transformation initiatives.

Owns marketing and go-to-market analytics, KPI definitions, attribution, campaign and sales-play measurement, scoring-model evaluation, dashboards, and weekly leadership reporting. The role reconciles Salesforce, HubSpot, and attribution data; partners with data teams on source-of-truth datasets; and builds AI-powered analytics agents, automated reporting, anomaly alerts, and data-quality processes to guide budget and pipeline decisions.

Analyze company data using SQL and Python to answer business questions, maintain trustworthy data pipelines, identify data-quality issues, and build automated dashboards, scripts, Slack bots, internal tools, and AI workflows. Partner with stakeholders to define needs, evaluate AI tools, improve processes, and document solutions for broader use.

Maintains and validates financial and securities reference data for clients, ensuring accuracy, completeness, and compliance. Responsibilities include monitoring investment data, evaluating vendors, documenting workflows, supporting data solutions, analyzing accounting and custody information flows, performing basic SQL tasks, and collaborating with clients, data providers, and internal teams. The role requires developing knowledge of fixed income, equities, corporate bonds, derivatives, and related investment processes.

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.

Conducts data analysis, forecasting, reporting, dashboard creation, and data integration to support business decisions. Defines data requirements and quality rules, analyzes trends and operational impacts, identifies improvement opportunities, and communicates insights to management and stakeholders. The role also performs audits, prepares presentations, and manages multiple analytical priorities.

Analyzes and manages Medicare Advantage eligibility, utilization, and benefit data. The role validates data accuracy, supports vendor data exchanges, troubleshoots production issues, performs SQL-based ad hoc analysis, and ensures compliance with CMS and organizational standards. It collaborates with product owners, vendors, IT teams, and business stakeholders to design scalable data solutions, communicate findings, manage requirements, and drive projects to completion.

Supports private-market asset data workflows by processing and reconciling data, monitoring exceptions, maintaining quality standards, analyzing trends, documenting operational logic, coordinating offshore teams, and partnering with Product, Data, and Engineering on process and product improvements.

Analyze, clean, validate, and reconcile data to produce dashboards, reports, metrics, SQL queries, analytical datasets, and business insights. Partner with stakeholders to define requirements, investigate trends and anomalies, validate data models and workflows, improve data quality, and establish scalable analytics standards and documentation.

Validate and review CO APCD-derived data products using SQL and visualization tools, perform output comparisons and project-level analyses, follow quality control standards, document findings, and communicate results to technical and non-technical stakeholders while supporting continuous improvement of data QA processes.

Partners with stakeholders to define analytics requirements, analyze complex data, build forecasting models, evaluate campaigns, and develop internal and external dashboards. Uses SQL and multiple data platforms to uncover business insights, identify drivers, and support data-driven decisions. Presents findings through clear narratives and visualizations while promoting data-driven collaboration and maintaining reporting solutions.

Analyze how customers use Hi Marley to guide product, customer success, and company decisions. Define feature success metrics, evaluate adoption funnels, measure pilots and utilization, present customer-facing insights, and establish trusted self-service reporting. Partner with Data Engineering to translate analyses into governed data models and communicate findings clearly to executives and customers.