QE Lead – Data & Integration Platforms

Essential functions

• Define the verification approach and stack for the integration and data platforms: what is checked, where each check runs, and which quality gate stops a bad change;

• Lead the end-to-end data-flow validation team across all program teams and partners;

• Agree the test strategy, quality gates, and SIT/UAT criteria with the client's QE Lead, and present coverage and results to client and program leadership;

• Work with architects, partners and the client so testability and contracts are designed in from the start, bringing options with their trade-offs and backing decisions with evidence;

• Build contract and API testing: Pact with can-i-deploy gates in every pipeline, breaking-change checks on API and event schemas, and simulated partner systems with contract suites that partners run themselves;

• Put data and money checks at every hop: data contracts, counts and totals per transfer, transformation tests, golden-dataset regression, and reconciliation of each data product to its sources before publication;

• Test resilience and load (idempotency, retries, dead-letter handling, cataloged volumes), run SIT across partners, and trace UAT defects to the component that caused them;

• Set QE standards for QA engineers in the platform teams, including how they use AI tools, and hire, grow, and manage the QE team.

Qualifications

• 8+ years in quality engineering, including 2+ years leading a QE or QA team;

• Has written a test strategy, defended it with a client, and seen it adopted;

• Hands-on: writes and reviews test code in Java and SQL;

• Depth in at least two of: backend and integration testing (REST APIs, events and queues, resilience, performance), contract testing (Pact), data pipeline verification (dbt, Great Expectations or similar, source-to-target reconciliation), cloud solutions (Azure preferred), and CI/CD quality gates;

• Uses AI coding tools (Cursor, Claude Code, Codex, or similar) daily and can set standards for how a team uses them;

• English at C1 or above; presents clearly to engineers and leadership and builds agreement across teams, partners, and architects.

Would be a plus

• Python

• QE leadership on a complex program with several teams or vendors

• Hands-on Azure and Microsoft Fabric

• PactFlow bi-directional contract testing and schema compatibility testing for events

• DataFrame-based validation on pandas or PySpark (Pandera, Soda, Deequ, chispa)

• Service virtualization (WireMock or similar)

• Experience with agent tooling: instruction files, MCP servers such as Playwright MCP, sub-agents

• Payments, settlement, or financial reconciliation

• Regulated or public-sector programs

• Data governance: data quality controls and lineage

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.