What you will accomplish in this roleWhat you will do:
Core Competencies
The following are highlighted entrepreneurial competencies and core expectations for the job/role:
- Strategic thinking and architectural leadership
- Deep technical expertise in cloud data platforms
- Strong problem-solving and systems design skills
- Ability to bridge business, clinical, and technical domains
- Clear written and verbal communication
This role will focus on setting technical direction on groups of applications and similar technologies as well as taking responsibility for the implementation of technically robust solutions encompassing all business, architecture, and technology constraints.
Technical Leadership & Architecture
- Define and own enterprise-scale data architecture patterns for multi-modal ingestion and Lakehouse platforms on Google Cloud Platform.
- Architect and evolve document-centric and unstructured data pipelines that support ingestion, enrichment, embedding, indexing, storage, and retrieval at scale.
- Lead the design of intelligent ingestion frameworks leveraging LLMs and advanced techniques (e.g., semantic chunking, embeddings, metadata extraction, classification, and enrichment).
- Establish architectural standards for cost-optimized, high-throughput ingestion across batch, streaming, and event-driven workloads.
- Drive platform designs that support search, analytics, and downstream AI/ML use cases in partnership with the AI/ML organization.
Lakehouse & Data Platform Design
- Architect and manage enterprise Lakehouse environments using technologies such as BigQuery, Apache Iceberg, Delta Lake, and GCS.
- Ensure strong design around schema evolution, ACID compliance, partitioning strategies, metadata management, and lifecycle policies.
- Optimize storage and compute usage to balance performance and cost across large-scale document and data repositories.
- Design data models and access patterns that support both analytical and AI-driven workloads.
Ingestion, Processing & AI Enablement
- Design and oversee ETL/ELT and ingestion pipelines using Dataflow, Dataproc, Pub/Sub, Cloud Run, GKE, and related services.
- Integrate AI/ML services into ingestion and processing pipelines for document understanding and content intelligence in partnership with that practice area.
- Partner with AI/ML teams to enable embedding generation, vector storage, and retrieval patterns aligned with enterprise governance standards.
- Ensure ingestion frameworks are resilient, observable, and designed for continuous evolution.
API Architecture & Governance
- Design and implement enterprise-grade REST and event-driven APIs aligned with OpenAPI specifications, versioning standards, and backward compatibility principles.
- Define and enforce API governance frameworks including naming conventions, authentication patterns, rate limiting, pagination strategies, and error handling standards.
- Architect API layers that serve both human-facing applications and AI agent consumers, ensuring consistent contract design across all downstream integrations.
- Collaborate with platform and engineering teams to establish API lifecycle management practices including deprecation policies, schema validation, and automated compliance checks.
- Design secure API access patterns for PHI-sensitive and regulated data, incorporating encryption, scoping, and audit logging requirements.
- Partner with vendor and internal delivery teams to review, validate, and approve API designs prior to implementation, ensuring alignment with enterprise architecture standards.
Governance, Security & Compliance
- Define and enforce best practices for data governance, security, privacy, and compliance (HIPAA, GDPR) across structured and unstructured data.
- Ensure architectural alignment with enterprise policies for data retention, lineage, access control, and auditability.
- Participate in and lead architectural design reviews to ensure adherence to standards and patterns.
Collaboration & Influence
- Collaborate with business, clinical, analytics, and engineering stakeholders to translate requirements into scalable architectural solutions.
- Provide architectural guidance for cloud migrations and modernization initiatives involving document and data platforms.
- Maintain a holistic view of enterprise information assets through diagrams, reference architectures, and technical roadmaps.
- Act as a technical mentor and advisor to senior engineers and architects.
What qualifications you will need:- Bachelor's degree in computer science, related technical field, or equivalent experience required
- Master's degree in computer science or related field preferred
- 3+ years of experience in Cloud Data or Information Architect required
- 5+ years of experience in Healthcare preferred
- 10+ years of experience in Information Technology required
A successful candidate will demonstrate:
- Deep experience designing enterprise data architectures on Google Cloud Platform or other Cloud Service Providers.
- Hands-on expertise with GCP services, including:
- BigQuery, Cloud Storage, Dataflow, Dataproc
- Pub/Sub, Cloud Run, GKE, Cloud Functions
- Bigtable, Cloud SQL, Cloud Spanner
- Strong knowledge of Lakehouse technologies and formats: Iceberg, Delta Lake, Parquet, Avro, JSON.
- Experience with document and unstructured data processing, including ingestion, enrichment, and indexing.
- Practical experience integrating LLMs and AI frameworks (e.g., Vertex AI, LangChain, embeddings, RAG patterns) into data pipelines.
- Proficiency in Python, SQL, and JVM-based languages (Java/Scala).
- Experience with CI/CD, DevOps, and infrastructure-as-code for data platforms.
- Strong understanding of data security, privacy, and regulatory requirements in cloud environments.
- Ability to analyze complex problems, modernize legacy pipelines, and design scalable solutions.
- Ability to communicate complex architectures clearly to both technical and non-technical audiences.
Certifications (a plus, but not required):
- GCP Professional Data Engineer
- GCP Professional Cloud Architect
PHYSICAL DEMANDS/WORKING CONDITIONS (Specific statements of physical effort required and description of work environment; e.g., prolonged sitting at CRT. required travel %)
Prolonged sitting or standing at computer workstation including use of mouse, keyboard, and monitor.
Requires ability to provide after-hours support.
