Top Data Analyst Jobs

Translate business needs into clear data requirements, design and maintain curated integrated datasets, validate mappings and lineage, support data governance and quality, enable data products for reporting and analytics, and lead small data integration and process-improvement initiatives across business, analytics, and engineering teams.

Support commercial performance, revenue management, customer analytics, sales operations, product strategy, and executive decisions. Build financial models, SQL data pipelines, quality controls, BI dashboards, automated workflows, customer health metrics, and product portfolio analyses. Partner cross-functionally with Product, Sales, Finance, Customer Care, Consulting, and leadership teams to deliver actionable insights and improve revenue retention, pricing, and product performance.

Build and maintain scalable batch and real-time data pipelines for financial and alternative datasets. Productionize AI/ML workflows for document extraction, classification, and data processing; own model evaluation, monitoring, and human review. Design investment data models, validation, anomaly detection, and reconciliation systems. Partner with quants and data scientists to operationalize research signals, onboard vendors and datasets, integrate APIs, and improve data reliability using modern cloud, streaming, orchestration, and warehouse technologies.

Analyzes customer and business requirements, data, test results, and program outputs to implement accurate batch data fulfillment solutions. Serves as a liaison between business, technical, and operational teams; configures systems, executes test plans, troubleshoots processing issues, maintains documentation, and ensures compliance with legal and operational standards. The role also supports process improvements, cost reduction, quality initiatives, and timely program delivery using proprietary systems.

Develops interactive dashboards, visualizations, and recurring reports that help cyber defense commanders and analysts understand risk, readiness, and mission impact. Creates autonomous visualization products, supports leadership’s ad hoc reporting needs, applies visualization best practices, and maintains templates and standards. Requires expertise with BI tools, Python visualization libraries, SQL, and data visualization principles, along with a DoD TS clearance and SCI eligibility.

Maintain and integrate investment data; build data systems and ETL pipelines; analyze large datasets; produce visualizations and reports (Tableau/Power BI/D3.js); improve data quality and governance; develop analytical tools; collaborate with clients, system architects, and product teams.

Build scalable data pipelines, dashboards, visualizations, and automated risk and compliance workflows using Python, Spark, Airflow, SQL, and generative AI. Perform exploratory and statistical analysis to identify insights, anomalies, and operational improvements. Partner with Operations, Compliance, Product, and Engineering to deliver data-driven solutions and support strategic initiatives.

Reviews, validates, organizes, and prepares asset datasets for reporting and visualization. Identifies missing, duplicate, inconsistent, or inaccurate information; applies data standards; documents corrections and exceptions; coordinates issue resolution; and supports repeatable data-management and process-improvement procedures. The role requires Excel proficiency, strong attention to detail, clear communication, independent work, collaboration, and a Top Secret clearance.

Designs and manages workforce surveys, facilitates focus groups, cleans and analyzes qualitative and quantitative data, and develops dashboards, visualizations, and executive presentations. Partners with stakeholders to identify trends, measure organizational effectiveness, and provide actionable workforce insights. Uses relational databases, visualization tools, and large language models to support longitudinal analysis and open-ended response analysis for Intelligence Community customers.

Own marketing analytics and measurement to improve acquisition, campaign performance, and marketing ROI. Lead multi-touch attribution, marketing mix modeling, A/B testing, incrementality studies, and KPI analysis. Partner with Marketing and Product to improve data quality, translate complex findings into recommendations, influence senior stakeholders, and coach peers on analytics practices.

As a Principal Data Analyst, you will innovate and manage data solutions, provide business intelligence, and ensure data quality for the Retail Bank.

Build and manage Power BI dashboards and reports for fundraising performance, analyze large datasets and KPIs, identify trends, improve data workflows, and translate findings into clear recommendations. Partner with fundraising, marketing, analytics, and leadership teams to support data-driven decisions. The role requires advanced SQL, strong data visualization, fundraising or financial analytics experience, and a relevant bachelor’s degree.

Supports strategic pricing through competitive price reports, Power BI dashboards, Snowflake data queries and joins, pricing performance tracking, sales deal evaluation, exception pricing requests, and ad hoc projects. Collaborates with Finance, Marketing, Product Management, and Sales while developing pricing strategy and analytical skills.

Analyze marketing and media data to measure TV and streaming campaign effectiveness, validate data and assumptions, identify actionable insights, and create clear visualizations and client-ready presentations. The analyst will use Excel, BI and data platforms, SQL, and AI tools to accelerate analysis while independently validating results and explaining methodologies, KPIs, and recommendations to internal teams and clients.

Analyze and visualize data, define product and business metrics, develop reliable data models and pipelines, and build self-service dashboards and datasets. The role supports product performance, user research, business strategy, and customer satisfaction while collaborating with data scientists, analysts, engineers, and cross-functional stakeholders. Responsibilities include maintaining data models, troubleshooting analytics solutions, and contributing through discussions and code reviews.

Analyze and visualize data to solve product, business, and operational problems. Define performance metrics with stakeholders, develop and maintain accurate data models, and build datasets, dashboards, self-service tools, and data pipelines. The role uses SQL, Python, Git, dbt, and Omni, supports geospatial and enterprise data analysis, and contributes to a collaborative team through discussions and code reviews.

Two-year rotational program for graduating students to analyze structured and unstructured data, develop dashboards and visualizations, apply SQL/Python/Tableau/Power BI, partner with business teams, and deliver insights that influence strategy and improve client and operational outcomes.

Two-year rotational program for recent graduates to analyze structured and unstructured data, build dashboards and visualizations, partner with business and tech teams, apply data governance, and deliver insights using SQL, Python, Tableau and cloud platforms to influence strategy and improve client and operational outcomes.

Analyzes large datasets to identify trends, variances, and revenue opportunities; builds data models, validates data quality, and supports pricing strategies. Creates management reports and presentations, collaborates with analysts, business partners, and IT, and provides actionable recommendations for revenue performance and strategic decision-making.

Designs and supports enterprise data integrations, ETL/ELT pipelines, APIs, data models, and transformations across Microsoft platforms. Maintains data quality, governance, security, and reliability while developing CI/CD pipelines, deployment automation, and release processes. Troubleshoots integration and deployment issues, manages environments, supports reporting and analytics, and collaborates with technical and business stakeholders to deliver scalable data solutions.

The Senior Data Analyst gathers BI requirements, analyzes enterprise and offline datasets, writes and optimizes SQL queries, and builds dashboards using Power BI, Tableau, and Domo. The role translates business needs into technical specifications, designs dashboard wireframes, validates data, documents processes, and collaborates with engineering, product, and business teams. Knowledge of the US trust business is essential for interpreting data within financial and regulatory contexts.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Leads Gartner research and thought leadership on data management, governance, and AI. Develops actionable insights, analyzes market trends, advises executive clients, delivers presentations, supports sales and conferences, reviews content, improves research processes, and mentors analysts. Requires deep expertise in data and analytics, AI implementation, data security, data value creation, executive communication, research, and strategic analysis. Remote U.S. role with travel up to 25%.

Conduct advanced workforce analysis for the Administrative Office of the U.S. Courts. Develop statistically defensible staffing methodologies, workforce baselines, scalable staffing models, staffing equations, and supporting tools. Apply statistical analysis, operations research, facility segmentation, scenario and sensitivity testing, and qualitative validation. Collaborate with security and data analytics subject matter experts to document assumptions, limitations, operational factors, and model performance.

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.