QA Architect / Lead
No salary listed
Expert
Bachelor's, Master's
Mumbai, Maharashtra, India
In Person
QA & TestingPerformance Testing, Test Automation Engineering
About the job
Requirements
- A bachelor's or master's degree in Computer Science, Engineering, or a related quantitative field.
- At least 8 years of QA experience, including at least 3–5 years focused on data quality, data warehousing, or data platform testing, and at least 2 years in a lead or architect role.
- Demonstrated experience defining and operating QA strategies for large-scale data platforms in cloud environments; GCP experience is preferred.
- Strong Python and SQL skills for test framework authorship, with familiarity with pytest and dbt-test conventions.
- Experience with data validation across batch, streaming (Kafka), and event-driven pipelines.
- Experience with dbt, including its 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, including schema evolution edge cases, partition handling, snapshot semantics, and copy-on-write versus merge-on-read behavioral differences.
- Experience with healthcare data quality testing, including HL7v2 parsing edge cases, FHIR profile conformance validation, terminology binding validation, and master data quality.
- Experience designing and executing load tests for data platforms at scale using JMeter, Gatling, Locust, or custom load generators, and familiarity with realistic load profile design across representative operations.
- Experience with parallel-run validation and reconciliation between legacy and replacement data systems.
- Strong CI/CD experience with GitHub Actions, GitLab CI, or Cloud Build, including designing pipeline gates and integrating test execution into pull request and release workflows.
- Analytical, communication, and collaboration skills to work across engineering, architecture, governance, and customer stakeholder groups.
Responsibilities
- Define and own the four-tier test framework for unit, integration, contract, and acceptance testing; establish conventions and CI gates that enforce them.
- Establish the spec-to-test traceability standard so every acceptance criterion in a unit specification has a named verifying test, test names reference the acceptance-criterion ID, and CI enforces coverage.
- Build and maintain test infrastructure, including fixture management for HL7v2, CCDA, CSV, and FHIR samples organized by source and dead-letter-queue scenario; dbt test runners; FHIR validator integration; Mini Cluster Flink test harnesses; HAPI test server harnesses for FHIR-Repository interceptor work; and end-to-end test orchestration.
- Establish synthetic test data conventions that exclude real protected health information, use structured but synthetic patient names, identifiers, and dates, provide reusable patterns for multi-source linkage scenarios, and cover edge cases such as NULLs, ECI merges, late-arriving data, and validation failures.
- Lead the load testing campaign by designing eight to ten scenarios that validate sustained 50K messages-per-second ingest, 150K messages-per-second peaks, full dbt refresh at billion-row scale, 1,000 concurrent Starburst queries, 5,000 requests-per-second FHIR API capacity, MDM degradation behavior, and reprocessing during steady state; lead execution and 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 data comparisons, accepted tolerances, reconciliation reports, and rollback triggers.
- Define performance service-level objectives and monitoring strategy, including measurable latency, throughput, and freshness budgets for every component; build dashboards operators use during incidents and to establish trend baselines.
- Define and enforce CI/CD test gates for pull request merges, nightly test runs, and stage promotion; codify them in pipeline configuration and runbooks.
- Build test automation for FHIR profile validation so every published Bundle is validated against US Core 6.1 in CI and at runtime; establish FHIR validator deployment, profile-pack management, and conformance test patterns.
- Lead testing for bidirectional FHIR flow, verifying loop prevention through origin tagging under load, capture of every client-originated change by the FHIR-Repository egress interceptor, correct application of write-authority rules by the loopback Flink job, and prevention of propagated echoes.
- Establish testing patterns for MDM integration by synthesizing match, no-match, and multi-match scenarios; validating ECI assignment correctness, ECI_MERGE and ECI_SPLIT handling end-to-end, and dead-letter-queue routing for MDM failures.
- Lead data-quality validation at every layer through dbt test suites for CDM, freshness checks at every layer boundary, anomaly detection for ingestion patterns, and governance integration to publish quality metrics to Atlan.
- Mentor data engineers on test authorship under the spec-driven development framework; ensure tests are part of every pull request and their quality is part of pull-request review.
- Participate in incident response and root cause analysis; drive preventive test additions so each production incident becomes an acceptance criterion in the affected specification and a new test in the suite.
Desired Qualifications
- GCP experience defining and operating QA strategies for large-scale data platforms in cloud environments.
- 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.
- Experience with test management tools such as TestRail, Zephyr, or qTest.
- Experience working in HIPAA-regulated environments, including synthetic protected health information generation, audit-trail validation, and regulatory test evidence.
- QA or cloud-platform certifications.
About the company
Quantiphi uses AI and digital engineering to help businesses transform by improving products, services, and operations. It designs and builds tailored AI, automation, and data analytics solutions based on client challenges and deploys them under long-term contracts with ongoing support. It differentiates itself by delivering end-to end, customized implementations and maintaining long client relationships, plus partnerships with Google Cloud for integrated deployments. Its goal is to embed AI to make organizations smarter, safer, and more efficient, driving digital transformation.
Company Size
1,001-5,000
Company Stage
Series A
Total Funding
$23.4M
Headquarters
Marlborough, Massachusetts
Founded
2013
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Simplify's Take
What believers are saying
- April 2026 Google named Quantiphi Partner of the Year across four categories.
- June 2026 Quantiphi achieved AWS Business Value Realization Competency, strengthening outcome-based selling.
- Revelio estimated 4,707 employees in March 2026, while hiring rose 69.3%.
What critics are saying
- Headcount fell 9.6% from 2023 to March 2026, showing services-margin pressure.
- Google Cloud and AWS control distribution; partner priorities can shift by 2027.
- If Google Cloud and AWS internalize agentic delivery, Quantiphi becomes replaceable.
What makes Quantiphi unique
- June 2026 AWS named Quantiphi First Preferred Quick SI Partner, signaling rare platform trust.
- August 2026 Google Cloud chose Quantiphi launch partner for Gemini Enterprise Financial Services.
- Candyspace's March 2026 acquisition adds human-centered design to Quantiphi's AI engineering stack.
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Benefits
- Professional Development Budget
- Conference Attendance Budget
- Performance Bonus