Top Senior Business Intelligence Jobs in Seattle, WA

Build and maintain Tableau dashboards, define KPIs, validate data, and query large datasets with SQL. Analyze cross-channel marketing performance across paid media, CRM, ecommerce, web, and app channels. Translate findings into actionable business insights, recurring client reports, and executive-ready presentations. Collaborate with engineering, analytics, data engineering, and strategy teams while guiding stakeholders toward data-driven decisions.

Transform data into actionable insights by building dashboards, reports, and visualizations. Partner with cross-functional teams and data engineering to ensure data quality, lineage, and AI-ready metadata. Audit datasets, define monitoring rules, document data dictionaries, pilot AI-assisted BI tools, and apply hypothesis-driven analysis to surface insights that inform business decisions.

Develops business intelligence and analytics solutions, including reports, dashboards, data marts, metadata layers, and decision-support applications. Analyzes data needs, designs dimensional and relational models, evaluates BI technologies, creates architecture documentation, and supports production troubleshooting. Partners with business and IT teams, manages small to midsize project scopes, establishes development standards, and mentors BI and analytics staff on tools, design practices, and data modeling.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Lead GTM strategy and analytics to build headcount/capacity models, territory optimization, quota setting, predictive models, and BI reporting. Partner with Sales, Marketing, Finance, and Data to inform planning cycles, monitor pipeline and productivity, and deliver executive-level insights for growth and investment decisions.

Lead data product strategy and governance for customer-facing reporting within Managed Services. Own reporting product roadmap, data quality, standards, delivery platforms (Redwood Insights, BI, Snowflake, Excel), implementation readiness, and cross-functional alignment to scale trusted, automated customer analytics and reduce manual reporting.

Design and govern enterprise semantic models in Microsoft Fabric and Power BI, author complex DAX and queries, enable NLQ/Copilot and AI integrations, coach report developers, lead stakeholder requirements, and implement governance, performance optimization, security, and AI-readiness across reporting domains.

Develop Power BI dashboards, reports, data models, and analytical tools for federal consulting engagements. Gather stakeholder requirements, integrate and validate operational data, create DAX calculations and Power Query transformations, and support forecasting, staffing, cost, inventory, and performance analysis. Optimize and document solutions, conduct testing and training, and transfer knowledge to Government users. Collaborate with logistics, operations research, security, and project management stakeholders.

Lead complex BI projects to define KPIs, build internal and client-facing Tableau/Power BI dashboards, enforce documentation and metric consistency, perform data analysis and profiling, create BI strategy and roadmaps, mentor junior analysts, and present insights to cross-functional and executive stakeholders.

Embed as the primary analytics partner on a digital product team to define KPIs, own measurement plans, build PowerBI dashboards, analyze member product behavior and funnels, design and evaluate A/B tests, write SQL/Python for analyses, and translate findings into actionable recommendations for product leaders and senior stakeholders.

Design, build, and maintain Power BI dashboards and data models using Snowflake and other cloud sources. Collaborate with data engineers/ETL teams, gather stakeholder requirements, implement DAX and Power Query transformations, ensure data governance and performance, and deliver analytics within Agile processes.

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.

The Senior BI Analyst will gather requirements, develop scalable reporting solutions, and create dashboards using Power BI, ensuring data-driven decisions across the organization.

The Market & Business Intelligence Manager will lead analytics strategies, manage a team of business analysts, and provide insights to support firm strategy and decision-making.

Manage enterprise BI and analytics platforms, data governance, access controls, and self-service reporting. Translate business requirements into governed dashboards, KPIs, metrics, and actionable insights using Power BI, Tableau, Azure, SQL, and multiple database technologies. Ensure data quality, platform scalability, business continuity, cost efficiency, and secure information delivery. Partner with business and technology stakeholders, lead process improvements, resolve complex issues, and mentor data professionals.

Designs and implements data integration, data warehousing, analytics and reporting solutions using Microsoft BI technologies. Develops T-SQL, SSIS, SSAS, and SSRS artifacts, works with third-party cloud monitoring tools, participates in Agile Scrum, provides technical leadership, and ensures high-performance, scalable, reliable reporting and analytics solutions.

Lead cross-functional analytics initiatives delivering accurate, trusted dashboards and metrics. Partner with Sales, Operations, Product, and Data Engineering to align analytics with business strategy, solve ambiguous problems, and mentor junior analysts.

Own end-to-end marketing and business intelligence: build automated dashboards (Domo), query large datasets (SQL/Snowflake), report subscription funnel economics, analyze channel performance and A/B tests, troubleshoot data anomalies, and partner with leadership to set KPIs and prioritize analytics work.

Support and develop BI applications for a client financial platform across environments. Build reports, analytics, and budgeting solutions (TM1), develop/tune Oracle SQL/PLSQL, deploy BI tooling, support UIs (low-code), follow Agile practices, and collaborate on integrations, security hardening, and operations.

Lead cross-functional analytics to inform clinical and operational decisions: build Tableau dashboards, create dbt analytical models, analyze large healthcare datasets (claims, EHR, HIE) with SQL, respond to ad-hoc requests via JIRA, and collaborate with product and engineering to improve data capabilities.

Design, develop, and maintain enterprise Power BI dashboards and reports. Integrate data from SQL Server, Azure, Excel and other sources, perform data modeling and DAX calculations, validate data, and document data models and reporting logic while partnering with business stakeholders.

Design, develop and maintain Microsoft BI solutions including SSAS tabular and multidimensional models. Build and optimize cubes, write MDX/DAX, apply partitioning, security, and DW/BI design best practices. Collaborate on architecture reviews, custom visualizations, and global-delivery projects.

Design, maintain, and automate GTM business data models, ensure data quality and observability, build BI-as-code pipelines (CI/CD, Git), create visualizations, and partner with Sales, Marketing, Finance, and Product to enable reliable reporting, analytics, and AI capabilities.

Build governed gold-layer data models and a single-source metrics layer in Snowflake (dbt); create and optimize dashboards in enterprise BI tools; use AI coding agents and AI-assisted tooling to accelerate development; ensure data quality, governance, and consistency; collaborate with data engineering and stakeholders to deliver trusted analytics for internal and client-facing use.

Architect and scale a governed BI and analytics platform (Snowflake, dbt, Sigma) to deliver trusted, standardized KPI frameworks and semantic layers. Build high-quality data models, optimize SQL for large datasets, enable self-serve analytics, mentor engineers, drive governance and testing standards, and partner with Product, Data Science, Finance, and business teams. Promote AI-assisted tooling to accelerate analytics delivery and improve metric trust and reporting consistency.