Senior Platform Engineer

Bill & Melinda Gates Foundation•Seattle, WA

2d•$157,400 - $236,000

About The Position

The Foundation is the largest nonprofit fighting poverty, disease, and inequity around the world, founded on the premise that people everywhere should have the chance to live healthy, productive lives. They believe in a diverse workforce and offer an exceptional benefits package. The Enterprise Data Solutions (EDS) team, within Business Operations and the CIO organization, leads work across Knowledge Management, AI, Data Engineering, Business Intelligence, Data Infrastructure, and Health Data Platforms. Their vision is to empower the Foundation with innovative, data-driven services and solutions for timely, informed decision-making. The EDS team recently launched the Enterprise Data Platform (EDP) as a central hub for managing, analyzing, and sharing the Foundation’s data assets and enabling AI. The Gates Foundation is seeking a Senior Platform Engineer to design and scale a modern data and AI platform to accelerate impact across global health, development, education, Gender Equality, and humanitarian initiatives. This role involves leading the development of a secure, governed, and scalable platform built on Azure, Databricks, and Snowflake, enabling advanced analytics and responsible generative AI solutions using OpenAI and Claude. The engineer will be critical in unlocking insights from structured and unstructured data and making them accessible through API-driven data services and intelligent search using Azure AI Search. The role also ensures data and AI systems are ethical, transparent, and aligned with global data governance standards, leveraging tools such as Collibra.

Requirements

Bachelor’s or master’s in computer science or related field or equivalent experience
Experience with analyzing and extracting insights from health and life sciences data (internal and external public repositories) using analytical and reporting tools
12+ years’ experience in platform engineering and/or software engineering, with a focus on advanced analytics, AI/ML Infrastructure, data security and automation
Manage and drive improvements of a lightweight custom application (Data Portal) for self-service data cataloging, search and discovery
Strong programming skills in Python, C#, .Net and SQL
Hands-on experience with: Azure (Data Lake, Azure ML, Event Hubs, etc.)
IaC automating infrastructure provisioning using Terraform
Databricks (Lakeflow, Spark, Delta Lake, workflows, Unity Catalog)
Snowflake (Iceberg, Horizon catalog, Cortex AI, secure data sharing)
Experience designing and building API-driven data platforms (REST/GraphQL, microservices)
Experience working with unstructured data processing
Familiarity with building data pipelines with Databricks and DBT Cloud and orchestration
Familiarity with Docker, Kubernetes, and distributed systems
Strong understanding of: RAG architectures, embeddings, and vector databases
Document chunking, indexing, and retrieval strategies
Experience with document processing/NLP pipelines (OCR, entity extraction, classification)
Collibra and Data Governance
Must have unrestricted work authorization in the country where this position is located. The Foundation does not provide immigration-related sponsorship for this role.

Nice To Haves

Previous experience with Salesforce would be a plus

Responsibilities

Lead the architecture and development of a cloud-native Data and AI platform on Azure, Databricks Lakehouse and Snowflake
Design and implement API-first data platform to enable secure, governed data sharing across teams, partners, and global stakeholders
Operationalize GenAI solutions for knowledge discovery and decision support, including: Retrieval-Augmented Generation (RAG) architectures, Integration with OpenAI and Claude APIs, Semantic and hybrid search using Azure AI Search
Create design patterns for document ingestion, parsing, OCR, metadata extraction, and enrichment
Design and implement embedding and vector search strategies for unstructured data
Lead the implementation of data governance and stewardship practices using Collibra, including: Data cataloging, lineage, and metadata management, Data quality frameworks and stewardship workflows, Alignment with global privacy, security, and compliance standards
Establish secure data access patterns (RBAC/ABAC), ensuring detection (PHI/PII scanning) and protection of sensitive data
Build reusable platform services, APIs, and tools that enable self-service analytics and AI development
Implement observability, monitoring, and evaluation frameworks for data pipelines and AI/LLM systems
Optimize platform performance, scalability, and cost efficiency via FinOps dashboards
Develop lightweight application for search and discovery, cataloging, and onboarding new datasets