Top Data Analyst Jobs in Seattle, WA

Deliver advanced SQL analyses, build and maintain dashboards and recurring reports, define KPIs, translate findings into executive-ready recommendations, partner cross-functionally, and support automation and reporting improvements.

Support warehouse operations by analyzing performance data, building dashboards and reports, automating recurring reporting, validating data, and providing actionable recommendations to improve productivity, labor utilization, inventory accuracy, and service performance. Partner with operations leadership for staffing, forecasting, and continuous improvement through statistical analysis and ad hoc investigations.

Run funnel audits and synthesize multiple datasets into exec-ready narratives. Recommend and help launch A/B tests, benchmark against genres and market data, partner with game analysts and ETL engineers to validate and maintain clean data, and provide actionable UA, monetization, and live-ops recommendations to customers.

Analyze business questions using SQL and Excel, build dashboards and reports, synthesize findings into clear presentations, and deliver actionable insights to senior stakeholders to drive strategy.

The SQL Data Analyst will focus on Business Intelligence development, data modeling, and advanced SQL query capabilities while working with Microsoft technologies.

We are seeking a DevOps Data Analyst with 8+ years of experience in CI/CD, BI development on Microsoft platforms, and advanced data analysis skills. Responsibilities include developing SSIS packages, SQL Server deployment, and OLAP Cube development.

The SQL Data Analyst will develop BI solutions, create scalable reports, and work with SQL Server and Microsoft BI technologies to enhance data visualization and analysis for clients.

Manage market and index data vendors, conduct due diligence, support data governance, partner with investment teams to resolve data issues, advise on licensing and contracts, and improve market data processes and controls to deliver scalable solutions.

The Marketing Data Analyst will analyze performance data across marketing and sales channels, develop dashboards, and provide insights to improve customer engagement and ROI.

The Senior Marketing Data Analyst will enhance marketing performance through data analysis, optimization strategies, and collaboration with marketing stakeholders, focusing on Checking and Deposit Acquisition.

The Data Analyst will maintain KPI standards for Customer Support, build dashboards, produce analyses, and lead metric alignment conversations, ensuring accurate data interpretation and decision-making support.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

The Business Intelligence / Data Analyst will optimize the data reporting pipeline using Google Big Query and Omni, work with product management, implement reports, and conduct ongoing maintenance.

Partner with GTM, Clinical Strategy, and Finance to build and maintain dbt models, client-facing reports, and dashboards. Perform SQL-based analyses, ROI and clinical outcomes reporting, and translate insights for non-technical stakeholders. Ensure data reliability through testing, code review, and collaboration with Data Engineering; support ad-hoc requests and automation of reporting.

As a Data Analyst II, you will analyze web and app traffic data to generate insights, develop metrics, evaluate experiments, and enhance user engagement and conversions.

Perform full-stack on-chain and product analytics: query on-chain data, profile ecosystem actors, benchmark protocols, define KPIs, build dashboards, and translate findings into actionable briefs for strategy, BD, product, and growth teams.

Build and maintain self-serve Tableau Next dashboards and reports, standardize metric definitions and semantic layer, perform ad-hoc and strategic analyses, own dbt semantic layer and metric governance, and partner with stakeholders to resolve reporting issues and improve reporting experiences for Customer Success and CCO leadership.

As a Data Analyst, you will create dashboards, analyze data, and ensure data quality while working with the VP of Finance.

Partner with product and business leaders to analyze insurance data for pricing, underwriting, retention, and competitive intelligence. Build and maintain SQL queries, Power BI dashboards, perform descriptive/diagnostic analyses, measure product changes, ensure data accuracy, and work autonomously on cross-functional initiatives.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.

Lead eCommerce analytics by building dashboards and self-serve reports, measuring funnel and revenue impact, performing deep-dive analyses, integrating and validating multi-source data, defining KPI standards, supporting experimentation, using AI-assisted tools to automate reporting, and communicating insights to web, marketing, and commerce stakeholders.

Design, develop, and maintain enterprise-scale Power BI dashboards and semantic models. Build advanced DAX measures and Power Query transformations, optimize report performance and refresh strategies, define KPIs and governance, partner with stakeholders, and drive analytics-driven improvements and automation to support business decision-making.

Analyze A/B experiments and large datasets to determine why experiments performed well or poorly. Communicate results to business partners, validate and improve data-driven decision processes, build key metrics and modeling reports, and collaborate with business and engineering stakeholders.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.