Top Tech Jobs & Startup Jobs in Seattle, WA

Leads transformation of life and annuity third-party administration operations, connecting technology and operations teams. Establishes financial and security controls, improves performance, reduces leakage, implements agentic AI and automation, manages transformation backlogs, and leads business analysts and actuarial resources. The leader also engages clients, supports onboarding to AI-enabled solutions, and may contribute to product transitions, block migrations, pre-sales, and commercial pricing.

Conducts insurance premium audits by scheduling client interactions, collecting and reviewing documentation, preparing accurate audit reports, and communicating with insureds and internal stakeholders. The role requires independent remote work, workload management, deadline adherence, and compliance with client and company standards.

Develops Power BI dashboards, SSRS reports, data models, KPIs, and healthcare analytics solutions. Uses advanced SQL and Python to transform, analyze, and optimize large healthcare datasets. Performs exploratory analysis, ensures data quality and governance, supports reporting and production issues, and communicates insights to technical, business, client, and executive stakeholders. Collaborates across Product, Engineering, Client Services, and Operations teams in Agile delivery environments.

Processes live check payments, deposits, banking transactions, reconciliations, and payment integrity records. Maintains check registers and financial documentation, investigates discrepancies, supports audits, and handles healthcare payment recovery activities. The role also performs data entry, reporting, provider outreach, and administrative support while ensuring compliance with internal controls, banking procedures, and HIPAA requirements.

Leads AI-enabled quality engineering and test automation across the SDLC. Builds automated UI, API, database, and batch testing frameworks; integrates intelligent testing into CI/CD pipelines; applies AI for test generation, defect prediction, root-cause analysis, regression optimization, anomaly detection, and quality monitoring. Partners with cross-functional teams, develops quality gates and dashboards, evaluates emerging AI tools, and mentors QA staff. Requires 5+ years of QA or test automation experience, programming proficiency, and hands-on AI/ML implementation.

Own enterprise data governance by defining data ownership, glossaries, dictionaries, metadata standards, quality controls, classifications, lineage, access governance, and data contracts. Partner with business, engineering, privacy, cybersecurity, and data product teams to resolve data issues, support AWS and Databricks modernization, manage schema changes, and establish governance policies, metrics, and accountability.

Design, build, and operate production-grade, event-driven data pipelines (Kafka/Flink) on GCP to deliver model-ready features. Optimize BigQuery SQL and Parquet performance, develop Python data workloads (Polars/Pandas), deploy ML pipeline components on Kubeflow/Vertex AI with Docker, design event store architectures, and collaborate with ML and platform teams while documenting architecture and standards.

Prepare, review, and send outgoing audit letters; monitor delivery confirmations and reprocess failures; handle chats and email correspondence; escalate issues, report system/workflow problems, and comply with HIPAA and quality/productivity standards to support the audit process.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.

Lead architecture, development, and optimization of a cloud-native data platform. Design high-volume batch and streaming pipelines, optimize data processing across AWS, Snowflake, Redshift, and Athena, and implement Iceberg-based data management. Establish security, privacy, observability, and DataOps practices while mentoring engineers, guiding technical direction, collaborating with data science teams, and delivering scalable petabyte-level data infrastructure.