Top Senior Data & Analytics Jobs in Seattle, WA

Leads the People data ecosystem, including Snowflake workforce data models, pipelines, governance, metric certification, and data quality. Develops scalable workforce analytics products using Visier and Snowflake, enables governed AI-driven insights, replaces manual reporting with self-service solutions, and manages a high-performing team of data and analytics professionals.

Design and maintain scalable data models, batch and streaming ETL pipelines, and end-to-end data workflows supporting business intelligence, machine learning, and operations. Ensure data quality, consistency, governance, traceability, and reliable delivery across distributed systems. Partner with business teams, platform engineers, and product managers to translate requirements into documented solutions and resolve data issues. The role is based in Seattle with three days per week in the office.

Own measurement strategy and instrumentation quality for web and mobile initiatives. Translate business goals into tagging specifications, KPIs, event schemas, and acceptance criteria; partner with marketing, product, engineering, and analytics teams; validate releases; troubleshoot tracking issues; govern data standards; and ensure behavioral data produces reliable, analytically accessible datasets and reporting.

Own analytics engineering for the machine infrastructure supply chain team. Build and maintain semantic layers, trusted data models, metrics, dashboards, and analytics-ready datasets using dbt and modern data platforms. Analyze inventory trends, optimize stock levels, investigate discrepancies, and develop supply chain KPIs. Partner with data engineering, finance, product, operations, planners, and buyers while improving inventory planning, replenishment, ERP processes, and warehouse efficiency.

Leads the strategy, architecture, and deployment of AI-enabled business intelligence infrastructure. Designs governed semantic layers, data models, and BI systems using dbt and enterprise data warehouses. Manages and coaches a technical engineering team, partners with executives and cross-functional stakeholders, and ensures reliable, high-performing data pipelines, warehouses, and reporting tools. The role also advances AI-driven analytics, data discovery, and self-service reporting across the organization.

Leads business intelligence and analytics engineering initiatives for Snap’s People Team. Designs scalable data pipelines, models, data products, dashboards, and self-service analytics solutions. Translates business needs into technical requirements, conducts statistical analysis, improves data quality and operational reliability, and delivers actionable insights. Owns systems end to end while mentoring team members and communicating effectively with executives, business partners, analysts, and technical teams.

Design and administer enterprise dashboards and reporting solutions (Tableau, Power BI, Microsoft Fabric). Translate complex business questions into compelling data stories, partner with stakeholders and BI engineers, champion visualization standards and governance, mentor analysts, and improve reporting usability and trust across the organization.

The Data Scientist in Finance will analyze financial data, create dashboards, collaborate on data engineering, and find improvements for the business.

Designs, develops, validates, and documents analytics data solutions for diverse client environments. Builds Snowflake, SQL, dbt, and AWS ingestion and transformation pipelines; monitors data quality and performance; troubleshoots complex technology issues; and supports reporting and business insights. Manages project scope, budgets, documentation, governance, client communication, implementation planning, and quality assurance while collaborating across teams and identifying opportunities for additional solutions.

Design and implement customer data solutions using Adobe Experience Platform and RTCDP. Configure XDM schemas, datasets, identities, and real-time profiles. Build ingestion pipelines, audience segmentation, and activation strategies. Ensure data quality, governance, and optimize platform performance while collaborating with marketing and technical teams.