QE Automation Lead

The team you'll be working with:
We are seeking an experienced QE Automation Lead to operate as the Test Lead for automated data testing within a complex, multi‑vendor data transformation programme.

This test role reports to the QE Lead (Test Director level) and is accountable for the design, execution and maintenance of the automation framework, data testing and overseeing the day‑to‑day management test activities of the Automation Test Engineers (SDETs) across multiple delivery teams in a Data Factory model.

The QE Automation Lead will come from a strong hands‑on technical background, with deep experience in test automation frameworks, AI driven solutions, data testing, and modern AWS cloud‑based data platforms. This role will lead a team of SDETs responsible for validating ETL / ELT pipelines on AWS and end‑to‑end data validation testing.

The role combines strong technical leadership, hands‑on contribution and team leadership, working collaboratively with the QE Lead to shape and refine the automation approach. Successful delivery is translating that direction into an effective, high‑quality implementation by the Automation Test Engineers (SDETs).

What you'll be doing:
Test Leadership & Delivery Ownership

Act as the Test Lead for automated data testing, accountable for planning, execution and reporting of automation activities.

Lead and manage a team of Test Engineers / SDETs, providing day‑to‑day direction, technical guidance and coaching.

Own automation scope, backlog and priorities in line with programme plans and milestones.

Ensure testing activity aligns with the overall Test Strategy and governance set by the QAT Lead.

Monitor progress that the Test Approach is being applied with sufficient auditable test coverage and results. This applies to Unit, System, Automated Interface/ E2E, Performance, Operational Acceptance, UAT data validation (Test Witness). This includes supporting Testing Quality Audits.

Automated Data Testing Strategy & Design

Design and implement an automated test approach in agreement with the QE Lead aligned to a Data Factory testing model leveraging AI augmentation.

Define and maintain automation test frameworks, standards and patterns for data testing.

Ensure automation provides sufficient, risk‑based test coverage and produces repeatable, auditable results.

Technical Hands‑On Contribution

Remain hands‑on supporting execution, contributing to:

Automated test scripts and AI utilities

Validation logic and data queries for data transformations

Test framework and supporting tooling

Lead testing of AWS‑based Lakehouse and data pipeline solutions, including:
AWS Glue Apache IcebergAI‑enabled ETL accelerators

Neo4jPython / PySpark, SQL and YAML‑driven configurations

Quality, Defect & Risk Management

Ensure quality issues, risks issues and data defects are identified early, clearly documented and effectively triaged.

Monitor, track and report on the test progress of several Factory teams by providing meaningful reports and dashboards to the QE Lead.

Analyse defect trends and test results to identify systemic quality risks.

Provide clear input to test exit decisions and go‑live readiness assessments.

Support formal Test Exit Reports and quality assurance reviews for quality gates, entry/exit criteria, test coverage expectations and acceptance thresholds.

Collaboration & Stakeholder Engagement

Work closely with the wider scrum team members:
Data Engineers, Platform Engineers, Product Owners, Business SMEs and third-party partners to ensure connected technical testing solutions.

Translate technical test outcomes into clear insights for the QE Lead.

Act as a technical quality authority within scrum and delivery teams.

Tooling, CI/CD & Test Data

Ensure effective use of test case and defect management tooling (e.g. ADO, JIRA, XRAY).Integrate automated tests into CI/CD pipelines (e.g. Git Hub Actions or similar).Support and guide test data management, including synthetic data creation, masking and environment readiness.

What experience you'll bring:
Extensive experience in data engineering/ test management.

Strong technical capability in:
Python / PySpark, SQL, Oracle, HDFS and YAMLBPSS minimum, SC not…