Data Architect - Healthcare Insights, Revenue Cycle
$140,000 - $190,000
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Posted 42 days ago
SENIORITY
Manager
SALARY
$140,000 - $190,000
About the role
- Architect and own the AI context platform
- Design end-to-end platform architecture: ingestion parsing/chunking enrichment embeddings vector indexing retrieval/serving
- Define scalable patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources
- Set technical direction for retrieval quality (query strategies, hybrid search, metadata filtering, reranking) in partnership with AI engineers
- Evaluate and select infrastructure, tooling, and cloud services to support platform needs across AWS/Azure/GCP environments
- Design and deliver semantic and governed data products
- Architect and implement semantic layers (metrics/entities) that power BI and agent reasoning consistently across the platform
- Define data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations)
- Establish standards for discoverability, documentation, and reusability across datasets and indexes
- Own the dbt or semantic layer tooling strategy and ensure consistent application across workstreams
- Operational excellence
- Own reliability and performance at the platform level: monitoring, alerting, SLAs/SLOs, runbooks, incident response, and postmortems
- Drive cost and latency optimization across Snowflake, lakehouse, and vector infrastructure
- Set engineering standards for CI/CD, testing, and evaluation (retrieval eval sets, regression tests, online telemetry)AI safety, governance, and compliance
- Implement security-by-design: RBAC/ABAC patterns, PII redaction, retention controls, audit logging, and safe access pathways for agent tools
- Partner with Security/Legal/Compliance to define and enforce guardrails for AI access to enterprise knowledge
- Own governance patterns for sensitive data handling across the platform
- Lead through influence
- Drive technical roadmap decomposition with product, AI, and application stakeholders
- Facilitate architectural decisions across teams and functions, building alignment without direct authority
- Set best practices and mentor engineers via design reviews, code reviews, and documentation
- Experience supporting LLM applications (RAG, agent tool interfaces, evaluation/observability)
- Knowledge of knowledge graphs, semantic modeling, or metrics layers at scale
- Experience in regulated environments and mature data governance programs
- Familiarity with Iceberg, Delta Lake, or other open table formats in a lakehouse context
- Prior experience in a formal or informal technical lead or staff engineer capacity
- Example Success Measures
- Measurable improvement in AI outcomes: higher retrieval precision/recall, better citation coverage, fewer "missing context" failures
- Reduced latency/cost per retrieval and improved platform reliability (SLO attainment, lower MTTR)
- Broad adoption of semantic definitions, context contracts, and platform standards across teams
- Architecture decisions are well-documented, defensible, and enable downstream engineers to deliver faster
- Platform design earns trust from Security, Compliance, and business stakeholders
- Behavioral Attributes
- Business-curious and domain-eager: Proactively learns healthcare processes, terminology, and KPIs — can speak credibly with SMEs and business leaders, not just translate requirements but help shape the right questions and success measures
- Stakeholder-first collaborator: Builds strong relationships with stakeholders, SMEs, and consultants; clarifies goals, constraints, and tradeoffs early; communicates progress and risks clearly; sets realistic expectations around timelines, scope, and quality
- Consultative problem-solver: Approaches requests with a "diagnose before prescribe" mindset — asks smart questions, proposes options, and guides teams toward durable solutions rather than one-off fixes
- Influence without authority: Leads through expertise and trust — drives alignment, facilitates decisions, and unblocks teams across functions without relying on positional authority
- High ownership and follow-through: Treats reliability, documentation, and operational readiness as part of the work; finishes what they start; holds a high bar for production quality
- Clear communicator for mixed audiences: Can go deep with engineers and explain concepts plainly to non-technical partners; writes crisp architecture docs, designs, and runbooks
- Pragmatic builder mindset: Biases toward shipping value in iterations, validating with users, and improving based on feedback — balancing innovation with maintainability and risk
- Comfortable with ambiguity: Thrives in early-stage or evolving spaces, adapts quickly, and turns unclear goals into actionable architectural plans
- Integrity and stewardship: Handles sensitive data responsibly, advocates for secure-by-design patterns, and enables the business to move fast without cutting corners on governance
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
