Principal Software Engineer - Integration & Data Architecture

GHX

12h•$150,000 - $214,000•Hybrid

About The Position

GHX is a healthcare supply chain exchange. At its core, what we do is move transaction data reliably between thousands of trading partners. Integration and data architecture are not supporting concerns here; they are central to how our product works and how we scale. This role owns the long-term stewardship of both problems: how data is modeled, accessed, and owned within our services, and how it flows reliably across them. You will work directly with application teams as a hands-on technical partner and architect, setting technical direction, establishing integration and data patterns, and ensuring the architectural quality we build today does not become tomorrow's maintenance burden. Your peers are strong backend engineers building real products. You earn trust through depth and direct engagement, not architecture diagrams from a distance.

Requirements

10+ years building distributed systems, grounded in hands-on software development as an individual contributor: you have written and shipped production code and understand the day-to-day engineering reality your guidance needs to hold up against
Deep experience with event-driven architecture and distributed systems: pub/sub and messaging infrastructure (Kafka, AWS SQS/SNS/EventBridge, or equivalents), integration patterns (outbox, CDC, saga, event sourcing), and failure modes including idempotency, consistency trade-offs, and operational failure handling
Hands-on AWS experience with data and integration services: SQS, SNS, EventBridge, DynamoDB, RDS/Aurora, API Gateway, Lambda
Strong data architecture fundamentals across relational and NoSQL: datastore selection by workload, schema and access-pattern design, ORM trade-offs, and performant data access at scale
Experience designing REST APIs with attention to versioning, backward compatibility, and data contracts, including contract validation in CI/CD pipelines
Experience working directly with application teams during service decomposition, and a track record of establishing integration and data standards across an engineering organization
Strong communication across levels: writing clear decision records for engineering peers, presenting trade-offs to engineering and product leadership, and translating technical risk for senior stakeholders
Heavy personal investment in AI-assisted and agentic development: your workflow has materially changed over the last 6-12 months, you use coding agents for substantial engineering work, and you have concrete examples of where these tools have made you meaningfully faster or produced better outcomes; the engineers around you tend to notice and follow

Nice To Haves

Experience integrating with external partners or enterprise platforms (supply chain, ERP, CRM, or similar), including migrating from batch- or file-driven pipelines to event-driven flows
Healthcare or other regulated industry experience
Azure PaaS experience (Service Bus, Cosmos DB, Azure Functions)
Infrastructure-as-code experience (Terraform or equivalents)
Analytics data layer experience: Snowflake, Redshift, or similar
Data architecture for ML/LLM applications (vector databases, embeddings, RAG patterns)
Prior Principal or Staff Engineer experience

Responsibilities

Define GHX's integration patterns: outbox, saga, CDC, event-carried state transfer, dual-write mitigation, and when to choreograph vs. orchestrate
Guide service boundary design: where to draw domain lines, how to handle shared reference data, and how to prevent shared-database coupling between services
Own the messaging and eventing layer: topic and queue design, schema contracts, partitioning, retention, consumer group management, dead-letter handling, replay, and operational monitoring
Lead the architectural direction for replacing GHX's homegrown event bus with a cloud-native messaging platform, and establish the standards teams will build on after the transition
Guide teams on streaming vs. batch trade-offs and on replacing batch- or file-driven pipelines with event-driven flows where latency and reliability require it
Establish REST API design standards: resource modeling, versioning strategy, error contracts, pagination, and backward compatibility
Own the API governance lifecycle: versioning policy, deprecation timelines, breaking change detection in CI/CD, schema compatibility enforcement, and consumer-driven contract testing
Define traffic management patterns for API transitions: versioned routing, canary rollouts, and gateway configuration for safe rollout and rollback
Guide teams on integration style selection and review service designs for evolvability and downstream interface cost
Work with teams on schema design, data modeling, and aligning data ownership with service boundaries
Guide datastore selection by workload: read/write patterns, consistency requirements, and cost trade-offs across relational, document, key-value, and analytical options; for NoSQL, lead access-pattern-first design including partition strategy, secondary index selection, and partition distribution
Advise on ORM vs. raw query trade-offs and define data access standards that avoid common anti-patterns (N+1, chatty calls, unbounded result sets)
Guide teams on performant data access and zero-downtime schema evolution: indexing strategy, backfill approaches, dual-write windows, and rollback-safe migrations
Establish replication and syndication patterns for cross-service data movement: CDC, change streams, materialized views, and read models
Run design reviews, workshops, and office hours to raise integration and data fundamentals across engineering
Produce decision records, design guides, and reference implementations teams can build from
Present architectural trade-offs and technical risk to engineering leadership, product, and senior stakeholders