Overview

General Position Information Reports directly to (Title):Manager Matrix reports to (Title):As applicable based on functional alignment Direct Reports:No Job Code:1875 Position Summary

Responsibilities

• Architectural judgment grounded in hands-on implementation
• Deep technical expertise in cloud data platforms
• Strong problem-solving, systems design, and software engineering skills
• Ability to bridge business, clinical, and technical domains
• Communication and interpersonal skills; influence without authority
• Understand strategic imperatives
• Design data architecture patterns for a defined platform or domain within the GCP Lakehouse ecosystem, and personally build the reference implementations that prove them.
• Write production-quality code (Python, SQL, Java/Scala) for ingestion frameworks, transformation pipelines, and platform tooling — not prototypes thrown over the wall, but modular, tested, reusable code that serves as the pattern others follow.
• Benchmark and validate architectural options empirically: build competing approaches, measure cost, throughput, and latency, and let evidence drive the standard.
• Spend meaningful time in the codebase alongside engineers — pairing, reviewing pull requests, and debugging production issues — to keep designs anchored in operational reality.
• Produce architecture decision records (ADRs), reference architectures, and diagrams that are backed by working artifacts in source control.
• Modernize and refactor legacy pipelines, leading migrations through direct contribution rather than delegation alone.
• Design and build Lakehouse environments using BigQuery, Apache Iceberg, Delta Lake, and GCS, including schema evolution, ACID compliance, partitioning, clustering, metadata management, and lifecycle policies.
• Model data and access patterns that serve both analytical and AI-driven workloads; implement the initial models and validate them against real query workloads.
• Optimize storage and compute for cost and performance across large-scale document and data repositories; instrument pipelines so cost and performance are observable, not estimated.
• Define and uphold SLAs for timely delivery of data, and build the monitoring that proves they are met.
• Design, build, and tune ETL/ELT and real-time ingestion pipelines using Dataflow, Dataproc, Pub/Sub, Kafka, Spark Streaming, Cloud Run, Cloud Composer, GKE, and Cloud Functions.
• Build multi-modal ingestion capabilities handling structured, semi-structured, and unstructured data (documents, text, images, PDFs, audio/video metadata).
• Implement AI-assisted ingestion patterns — semantic chunking, embedding generation, metadata extraction, classification, and enrichment — in partnership with the AI/ML practice, and integrate them into pipelines hands-on.
• Partner with AI/ML teams on vector storage and retrieval (RAG) patterns aligned with enterprise governance standards.
• Ensure ingestion frameworks are resilient, observable, idempotent, and designed for continuous evolution.
• Design and implement REST and event-driven APIs aligned with OpenAPI specifications, versioning standards, and backward compatibility principles.
• Apply enterprise API governance — naming conventions, authentication patterns, rate limiting, pagination, and error handling — and contribute the shared libraries and templates that make compliance the default.
• Build API layers serving both human-facing applications and AI agent consumers, ensuring consistent contract design across downstream integrations.
• Implement secure API access patterns for PHI-sensitive and regulated data, incorporating encryption, scoping, and audit logging.
• Review and validate API designs from vendor and internal delivery teams prior to implementation.
• Champion test-driven development, continuous integration, and automated deployment by building them into the platforms this role delivers.
• Implement unit and integration tests; conduct performance testing where appropriate.
• Build and maintain CI/CD pipelines and infrastructure-as-code (GitHub, Terraform) for data platforms.
• Implement automated workflows that lower manual/operational costs and move the company closer to democratizing data.

Education

• Bachelor's degree in computer science, related technical field, or equivalent experience Required
• Master's degree in computer science or related field Preferred
• 7+ years of experience in Data Engineering, Data Architecture, or equivalent hands-on data platform development Required
• 2+ years of experience in a Cloud Data or Information Architect capacity Required
• 3+ years of experience in Healthcare Preferred
• 8+ years of experience in Information Technology Required
• Hands-on experience designing and building enterprise data solutions on Google Cloud Platform or another major cloud provider — with a portfolio of systems personally built, not only specified.
• Must-have skills/tools:
• Python and SQL at production depth; JVM-based languages (Java/Scala)
• BigQuery, Cloud Storage, Dataflow, Dataproc, Cloud Composer
• Pub/Sub, Kafka, Spark Streaming
• Cloud Run, GKE, Cloud Functions
• Lakehouse formats: Iceberg, Delta Lake, Parquet, Avro, JSON
• CI/CD pipelines, Git/GitHub, test-driven development
• Nice-to-have skills/tools:
• Terraform, Docker, Kubernetes, Dynatrace
• Bigtable, Cloud SQL, Cloud Spanner
• Microservices architecture, OpenAPI/REST and event-driven API design
• Vertex AI, LangChain, embeddings, RAG patterns
• Healthcare domain knowledge/experience
• Experience with document and unstructured data processing, including ingestion, enrichment, and indexing.
• Practical experience integrating LLMs and AI frameworks into production data pipelines.
• Strong understanding of data security, privacy, and regulatory requirements (HIPAA, GDPR) in cloud environments.
• Strong ability to assemble large, complex data sets meeting functional and non-functional requirements.
• Strong ability to identify, design, and implement internal process improvements, including redesigning data platforms for greater scalability and optimized data delivery.
• Expert ability using source control management and CI/CD automation tools.
• Strong understanding of Agile methodologies and how to apply Agile within the team.
• Ability to communicate complex architectures clearly to both technical and non-technical audiences, and to present and facilitate technical ideas.
• Proven ability to complete work, make sound decisions, and plan and accomplish goals without explicit direction/guidance from leadership.
• Demonstrates an empathetic and growth mindset with a willingness to learn new skills, technologies, and methodologies.