Top Data & Analytics Jobs in Seattle, WA

Leads advanced analytics, reporting, modeling, dashboards, and data storytelling to support executive decisions. Identifies business trends and performance drivers, develops recommendations, frames analytical questions, and collaborates cross-functionally to improve customer experiences, resource prioritization, and organizational strategy.

An 11-week paid internship supporting T-Mobile’s Wholesale & Roaming performance intelligence team. The intern will analyze and integrate data, build dashboards, evaluate financial and operational trends, investigate forecast variances, assess growth and pricing opportunities, improve data quality, automate reporting, and present actionable recommendations to business, technical, and financial stakeholders.

Leads strategy, architecture, governance, sourcing, and delivery of training and calibration data for data science and pricing teams. Oversees data engineering and pipeline modernization, vendor and warehouse integration, source-system changes, data lineage, and model-ready dataset delivery. Manages the Modeling Data Solutions organization, partners with enterprise data stakeholders, and guides teams supporting business-line data needs.

Analyzes complex regulatory data requests across insurance data sources, translates ambiguous guidance into precise requirements, collaborates cross-functionally, and delivers defensible findings. Builds reporting for leadership, documents data logic, and improves processes through automation, AI-assisted analysis, and reusable data practices. Leads projects independently and serves as a trusted expert in underwriting data and business processes.

Build and operate paid media data pipelines that ingest, model, monitor, and reconcile advertising spend, performance, attribution, and web analytics data. Develop scalable distributed systems and marketing utilities using AWS and modern data technologies. Integrate advertising APIs, support dashboards and reporting, ensure data quality, and adapt pipelines as platforms evolve. Partner with marketers, analysts, and leadership to define solutions, while mentoring engineers, leading code reviews, and managing complex projects.

Build econometric and causal inference models to measure marketplace effects, customer lifetime value, supplier behavior, and product changes. Apply panel data, difference-in-differences, regression discontinuity, instrumental variables, synthetic controls, and experimental design. Partner with ML teams, data scientists, economists, and product leaders; translate findings into visualizations and recommendations for pricing, ranking, and recommendation systems.

Leads end-to-end people analytics projects, translating HR and business priorities into metrics, statistical analyses, visualizations, and executive recommendations. Partners with HR leaders, product teams, machine learning scientists, and data engineers to improve organizational insights, data quality, analytics products, and self-service capabilities. Designs employee listening programs, delivers data-literacy coaching, manages analytics intake, supports product development, and advances analytical practices across the People Analytics team.

Designs and operates enterprise-scale data platforms, including batch and streaming pipelines, transformation and orchestration workflows, analytical data warehouses, telemetry systems, data catalogs, semantic layers, and AI-supporting data interfaces. The role requires production monitoring, incident support, architectural decision-making, and collaboration with engineering, analytics, data science, and business stakeholders.

Investigate Terms of Service and Community Guidelines violations, identify abuse trends, and develop automated detection and enforcement rules. Analyze large datasets using SQL, create dashboards and reports, measure mitigation effectiveness, and recommend policy, process, and product improvements. Collaborate with Product, Engineering, and scaled review teams, apply LLM-based approaches, provide training, and improve internal investigation tools and safety operations.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Build and maintain analytics data systems and pipelines that capture, transform, and integrate product usage data at scale. Deliver accurate insights and visualizations to inform product decisions, collaborate cross-functionally, and apply AI tools to improve analytics and workflows.

Leads complex fulfillment-center analytics and applied AI initiatives from problem definition through production adoption. Builds scalable data models, pipelines, inspection tools, root-cause analysis, predictive solutions, and self-service analytics. Partners with Product, Engineering, Data Science, and Operations, establishes engineering standards, conducts reviews, mentors junior engineers, and communicates recommendations to senior stakeholders. Requires strong SQL, Python, cloud data, machine learning, visualization, testing, and software engineering expertise.

Build and maintain scalable GTM data pipelines, marts, semantic models, and dashboards supporting headcount planning, quota attainment, pipeline, and sales effectiveness. Partner with GTM analysts and stakeholders to establish reliable metrics, enable data tools, document systems, and develop AI-powered natural-language data querying capabilities. Translate complex data concepts for executives and engineers while ensuring high-quality, maintainable data products.

Build and maintain Databricks data pipelines and reusable models for Go product analytics. Define metrics, improve event logging, connect product usage with customer data, and support adoption and growth analysis. Ensure data quality through testing, monitoring, troubleshooting, and documentation. Optimize pipeline performance and costs while collaborating with Product, Engineering, and Data Science. Contribute dashboards and analyses that support product and business decisions.