Quality Engineering Lead

Grid Dynamics; Województwo pomorskie; On-site; PLN 300,000 - 540,000; Full time

Benefits offered by this job

Bleeding-edge projects

Motivated team

Competitive salary

Flexible schedule

Medical insurance

Sports

Corporate events

Professional development

Well-equipped office

Job summary

Grid Dynamics is seeking a Quality Engineering Lead (National Scale & Data Integrity) to define and execute the end-to-end quality strategy for a greenfield, national-scale recycling platform connecting tens of thousands of IoT devices and data platforms.

You will lead an E2E data-flow validation team, collaborate with client leadership and vendors, and stay hands-on with test automation, contract testing, data integrity verification, and resilience testing.

Qualifications

• 8+ years total QE experience with 2+ years leading QE teams on multi-vendor or complex platforms.
• Hands-on REST API testing, event-driven pipelines, and consumer-driven contract testing using Pact (tests, provider verification, broker, can-i-deploy).
• Data platform validation using Python/Java (Great Expectations, pytest+chispa, Pandera, or dbt tests) and source-to-target reconciliation.

Responsibilities

• Define and defend the verification approach for integration and data platforms with automated quality gates.
• Lead cross-vendor E2E data-flow validation across partner vendors to automate data integrity and tracing flows.
• Act as the primary technical contact to client QE leads, vendors, and architects, negotiating test strategies and SIT/UAT criteria.
• Implement consumer-driven contract testing (Pact / PactFlow) and schema checks for REST APIs and event-driven architectures.
• Build data quality checks at ingestion and transformation stages using DataFrame-based validations (Great Expectations, chispa) to reconcile KPI data.
• Design resilience, idempotency, fault injection, and load tests for high availability and reliable messaging.
• Establish AI-assisted engineering standards and oversight for quality definitions.
• Hire, mentor, and manage QE engineers across platform teams and report quality metrics.

Skills

Quality engineering leadership

REST API testing

Data pipeline validation

Python/Java

Azure DevOps / GitHub Actions

English communication

Tools

Pact

PactFlow

Great Expectations

pytest

chispa

dbt tests

Azure DevOps

GitHub Actions

Job description

We are seeking a Quality Engineering Lead (National Scale & Data Integrity) to join a high-impact engineering team partnering with a Tier 1 UK Public Infrastructure & Environmental Organisation. In this role, you will define and execute the end-to-end quality strategy for a greenfield, national-scale recycling platform connecting tens of thousands of IoT return machines, core systems, financial clearing networks, and data platforms. You will lead an E2E data-flow validation team, acting as the primary point of contact for client leadership, partner vendors, and architects, while staying hands-on with test automation, contract testing, and data integrity verification.

Responsibilities

• Verification Strategy & Quality Gates: Define and defend the verification approach for the integration and data platforms, establishing robust automated quality gates across Azure DevOps/GitHub Actions pipelines.
• Cross-Vendor E2E Data Flow Validation: Lead the E2E data-flow validation team across multiple partner vendors to automate data integrity, tracing money and container flows across all system hops.
• Client & Stakeholder Leadership: Act as the primary technical point of contact and counterpart to the client's QE Lead, negotiating test strategies, quality gates, and SIT/UAT criteria with evidence and trade-off analysis.
• Contract & Schema Testing: Implement consumer-driven contract testing (Pact / PactFlow) and schema compatibility checks for REST APIs and event-driven architectures to prevent breaking changes.
• Data Platform Verification & Reconciliation: Build data quality checks at every ingestion and transformation stage, utilizing DataFrame-based validations (Great Expectations, chispa) to reconcile KPI and regulatory data products to source data.
• Resilience & Performance Engineering: Design resilience, idempotency, fault injection, and load tests to ensure high system availability, dead-letter recovery, and reliable handling of duplicate messaging.
• AI-Assisted Engineering Standards: Establish best practices for AI-driven software development (Cursor, Claude Code, Codex) and agent harnesses within the QE organization, ensuring human oversight for quality definitions.
• Team Leadership & Growth: Hire, mentor, and manage quality engineers across platform teams, driving test code reviews and reporting quality metrics through completed flows and financial reconciliation.

Requirements

• Professional QE Leadership: 8+ years of total experience in Quality Engineering, including 2+ years leading QE teams on multi-vendor or complex platform engineering programs.
• Contract & API Verification: Hands-on experience with REST API testing, event-driven pipelines, and consumer-driven contract testing using Pact (tests, provider verification, broker, can-i-deploy).
• Data Pipeline Quality: Strong background in data platform validation using Python/Java frameworks (e.g., Great Expectations, pytest + chispa, Pandera, or dbt tests) and source-to-target data reconciliation.
• Language & Tech Stack: Hands-on proficiency in Java and SQL (Python is a strong plus), with solid knowledge of major cloud services (Azure preferred: API Management, Event Hubs, Service Bus, Functions).
• CI/CD & AI-Assisted Tooling: Deep experience configuring quality gates in Azure DevOps or GitHub Actions, alongside daily usage of AI coding assistants (Cursor, Claude Code, or Codex).
• Executive Communication & Soft Skills: Fluent English (C1+) with proven capability to present complex quality strategies, negotiate with external vendors, and align cross-functional leadership.

Nice to have

• Azure & Data Platform Expertise: Direct hands-on experience with Azure managed services and Microsoft Fabric.
• Contract Testing Strategy: Practical experience with PactFlow bi-directional contract testing.
• Financial & Regulatory Domain: Domain experience in payments, financial settlement, container/unit-level reconciliation, or regulated public-sector platforms.
• Data Governance: Strong familiarity with data lineage, data quality controls, and compliance reporting.

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.