Define and own the four-tier test framework: unit (per spec acceptance criterion), integration (within-layer composition), contract (cross-layer schema and behaviour), and acceptance (end-to-end scenarios). Establish the conventions that distinguish them and the CI gates that enforce them
Establish the spec-to-test traceability standard. Every acceptance criterion in a unit spec must have at least one named test that verifies it; test names reference the AC ID by convention; CI enforces coverage. The traceability is regulatory-defensible by design
Build and maintain the test infrastructure — fixture management (HL7v2, CCDA, CSV, FHIR samples organized by source and DLQ scenario), DBT test runners, FHIR validator integration, Mini Cluster Flink test harnesses, HAPI test server harnesses for FHIR-Repository interceptor work, end-to-end test orchestration
Establish synthetic test data conventions — no real PHI in fixtures; structured-but-synthetic patient names, identifiers, dates; reusable patterns for multi-source linkage scenarios; coverage of edge cases (NULLs, ECI merges, late-arriving data, validation failures)
Lead the load testing campaign — design the eight to ten scenarios that validate sustained 50K msg/sec ingest, 150K msg/sec peaks, full DBT refresh at billion-row scale, 1,000 concurrent Starburst queries, 5,000 req/sec FHIR API, MDM degradation behaviour, and reprocessing during steady state. Lead execution; report against acceptance criteria
Lead the parallel-run validation framework — for the 30-60 day cutover period when the new platform ingests alongside the existing Health Data Engine, establish what data is compared between the two systems, what tolerances are accepted, what reconciliation reports are produced, and what triggers a rollback decision
Apply for this job in 1 click
Skip the repetitive application forms
Install the Base Career Chrome Extension and autofill job applications across major job boards with your profile.
Trusted by over 500,000 job seekers on Base Career
Define performance SLOs and monitoring strategy. Every component has measurable latency, throughput, and freshness budgets. Build dashboards that operators use during incidents and that establish trend baselines over time
Define and enforce CI/CD test gates — what blocks PR merge (spec lint, AC coverage, unit tests, integration tests, lint/format), what runs nightly (full integration suite, contract tests, slow scenarios), what runs in stage before promotion (acceptance tests). Codify in pipeline configuration and runbooks
Build out test automation for FHIR profile validation — every Bundle published is validated against US Core 6.1 in CI and in the runtime path. Establish the FHIR validator deployment, the profile pack management, and the test patterns that prove conformance
Lead testing for the bidirectional FHIR flow — verifying loop prevention via origin tagging works under load, that the FHIR-Repository egress interceptor captures every client-originated change, that the loopback Flink job applies write-authority rules correctly, and that no echoes propagate
Establish testing patterns for MDM integration — synthesizing match/no-match/multi-match scenarios, validating ECI assignment correctness, validating ECI_MERGE and ECI_SPLIT handling end-to-end, validating DLQ routing for MDM failures
Lead validation of data quality at every layer — DBT test suites for CDM, freshness checks at every layer boundary, anomaly detection for ingestion patterns, governance integration for quality metric publication to Atlan
Mentor data engineers on test authorship under the spec-driven development framework. Tests are part of every PR; their quality is part of the PR review; the QA lead sets the bar
Participate in incident response and root cause analysis. Drive test additions that prevent recurrence — every production incident becomes a new acceptance criterion in the affected spec and a new test in the suite
Required Skills and Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field
8+ years of QA experience with at least 3-5 years focused on data quality, data warehousing, or data platform testing, and 2+ years in a lead or architect role
Demonstrated experience defining and operating QA strategies for large-scale data platforms in cloud environments — preferably GCP
Strong Python and SQL skills for test framework authorship. Familiarity with pytest and dbt-test conventions required
Experience with data validation across batch, streaming (Kafka), and event-driven pipelines
Experience with dbt — specifically dbt's native testing framework, dbt-expectations or dbt-utils packages, custom test authorship via macros, and source freshness checks
Experience testing Iceberg or other open-table-format-backed warehouses — schema evolution edge cases, partition handling, snapshot semantics, and CoW vs MoR behavioral differences
Experience with healthcare data quality testing — HL7v2 parsing edge cases, FHIR profile conformance validation, terminology binding validation, master data quality
Experience designing and executing load tests for data platforms at scale — JMeter, Gatling, Locust, or custom load generators. Familiarity with realistic load profile design (not just throughput, but representative mix of operations)
Experience with parallel-run validation and reconciliation between legacy and replacement data systems
Strong CI/CD experience — GitHub Actions, GitLab CI, or Cloud Build. Ability to design pipeline gates and integrate test execution into PR and release workflows
Excellent analytical, communication, and collaboration skills. The role works across engineering, architecture, governance, and customer stakeholder groups
Nice-to-Have Skills
Experience with the HL7 FHIR validator, Inferno, or HAPI FHIR validation tooling
Atlan experience for data quality metric publication and governance integration
Experience with chaos engineering or resilience testing for streaming data systems
Experience integrating spec-driven or contract-first frameworks with QA workflows
Test management tools experience (TestRail, Zephyr, qTest)
Experience working in HIPAA-regulated environments — synthetic PHI generation, audit trail validation, regulatory test evidence
QA or cloud platform certifications
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !
About Quantiphi
IT Services & Consulting4303 employeesFounded 2013
AI-first digital engineering company helping enterprises solve complex business problems with artificial intelligence, cloud, and data engineering.
