Associate Lead Testing

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If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

QA Architect / Lead

Exp Range : 8 - 13 Years

Job location : Mumbai , Bangalore, Trivandrum

The QA Architect/Lead is responsible for the testing strategy and infrastructure across the entire platform. You will define the four-tier test framework (unit, integration, contract, acceptance), establish the conventions that ensure every spec-defined acceptance criterion has a verifying test, build out the validation infrastructure for parallel-run cutover from the existing Health Data Engine, and lead the load testing campaign that validates the platform's high-volume

capacity claims.

This role is technical-lead calibre. You will design test frameworks that data engineers consume, set the CI/CD gating policies that determine what merges and what doesn't, and lead the validation that determines when the platform is ready for production traffic. You will work in close partnership with the architect on spec-derived test conventions and with the data engineering team on the operational maturity of every component.

Key Responsibilities

• 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

• 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