Top Tech Jobs & Startup Jobs in Seattle, WA

Lead enterprise data engineering standards, governed pipelines, data contracts, observability, quality controls, lineage, testing, and compliant data delivery. Partner with governance, privacy, architecture, modeling, and platform teams to ensure reliable, traceable, protected data products. Monitor operational performance, automate deployment and validation, and resolve pipeline and data reliability issues across complex enterprise environments.

Lead enterprise data governance capabilities across KPI governance, governance platforms, or data access and security. Responsibilities include defining frameworks and controls, managing governance platforms and access policies, partnering with business, technology, privacy, legal, and compliance teams, overseeing vendors, and reporting governance maturity and operational health. The role supports data strategy, trusted data access, Data Command Center capabilities, and responsible data use in a regulated environment.

Leads the strategy, architecture, development, deployment, and optimization of an agentic AI platform. The role transforms products and workflows into scalable AI experiences, oversees AI engineering operations, establishes security and governance practices, evaluates models and orchestration frameworks, and drives observability and continuous improvement. The Associate Director partners with product, data, architecture, security, and business teams while building and mentoring high-performing engineering teams.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Lead enterprise metadata strategy, standards, governance, capture, curation, validation, lineage, and lifecycle management across data products and business assets. Partner with data governance, engineering, quality, analytics, modeling, and stewardship teams to improve data trust, discoverability, reuse, and AI readiness. Drive metadata automation, catalog synchronization, certification support, issue remediation, and stakeholder communications across a complex enterprise data environment.

Lead enterprise data quality practices by defining standards, rules, thresholds, scorecards, dashboards, and control frameworks. Partner with governance, engineering, product, and business teams to establish measurable requirements, embed controls across data products, monitor critical data assets, and resolve quality issues through root-cause analysis and remediation. Support AI-ready data initiatives through trusted datasets, lineage, certification, and governed usage while maintaining documentation and promoting continuous improvement.

Design and govern conceptual, logical, and physical enterprise data models supporting analytics, reporting, AI-ready datasets, and certified data products. Translate business requirements into scalable data structures across customer, product, payer, and territory domains. Establish modeling standards, review models against Common Data Model standards, maintain metadata and lineage accuracy, and resolve data gaps and inconsistencies. Collaborate with business, engineering, and technology stakeholders to provide modeling expertise and design guidance.

Leads enterprise analytics engineering by establishing statistical standards, validation and QA practices, measurement frameworks, reporting standards, governance, reusable data pipelines, automation, and production-grade analytics solutions. Connects Commercial, Medical, Market Access, and other teams to enable scalable, trusted, compliant, and decision-focused analytics. Requires strong technical and cross-functional leadership, analytics infrastructure expertise, and the ability to build enterprise capabilities from ambiguity.

Leads the long-term strategy, innovation roadmap, and ecosystem partnerships for NovaOS, an enterprise AI platform. Identifies emerging technologies and market opportunities, develops AI-enabled concepts and business cases, manages strategic partnerships, and transitions validated innovations into product roadmaps. Establishes governance and investment frameworks, collaborates with Product, Applied AI, Engineering, UX, and Commercial teams, and builds a high-performing strategy and innovation organization.

Designs, builds, and governs enterprise ontologies, taxonomies, semantic models, knowledge graphs, metadata services, and semantic APIs. Partners with product, AI, analytics, platform, and business teams to enable RAG, agentic AI, contextual search, reasoning, interoperability, and analytics consistency. Establishes metadata governance, quality controls, stewardship, lifecycle management, and reusable engineering patterns. Evaluates semantic technologies, mentors engineers, and communicates technical trade-offs and recommendations to diverse stakeholders.