EBRD
Principal, Quality Eng; Data & Analytics
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Requisition ID
36990
Office Country
United Kingdom
Office City
London
Division
Information Technology
Contract Type
Fixed Term
Contract Length
3 years
Posting End Date
24/08/2026
Principal, Data and Analytics Quality Engineering Lead
Shape how quality is engineered across mission-critical financial platforms.
This is a rare opportunity to lead Quality Engineering at scale, driving intelligent automation, embedding quality into every stage of delivery, and redefining how enterprise data, integration and analytics platforms are tested, validated and trusted. You’ll operate across a diverse technology landscape spanning Microsoft Fabric, EBX, Azure Integration Services, Informatica IDMC and Control-M orchestration, bringing together modern QE practices, AI-driven insights and deep domain expertise to ensure resilience, accuracy and performance where it matters most.
If you’re passionate about transforming quality from a checkpoint into a strategic engineering capability and want to make a visible impact in a complex, regulated environment, this is your moment.
What You’ll Do
- Define and lead Quality Engineering strategy across the Data & Analytics platform estate, ensuring quality is embedded from design through to operational support across data, integration, master/reference data and reporting capabilities.
- Lead platform-wide assurance across Microsoft Fabric, EBX, Azure Integration Services, Informatica IDMC and Control-M, ensuring consistent quality standards, automation patterns and governance controls across the full technology landscape.
- Embed automation-first testing into CI/CD pipelines, covering functional, regression, reconciliation, interface, data quality and operational validation across cloud data platforms and integration services.
- Define validation approaches for Microsoft Fabric workloads, including Lakehouse and Warehouse structures, Medallion Architecture flows, Data Factory pipelines, notebooks, semantic models, Power BI outputs and Direct Lake reporting patterns.
- Establish QA practices for EBX master and reference data capabilities, including workflow validation, hierarchy testing, data governance rule validation, reference data integrity and integration with downstream consuming platforms.
- Define assurance patterns for Azure Integration Services, including API and interface testing, message validation, event-driven integration, retry handling, exception paths, monitoring, alerting and resilience scenarios.
- Lead QA for Informatica IDMC and related data integration services, including ingestion, transformation, reconciliation, lineage validation, data quality rules and migration assurance from legacy platforms where applicable.
- Shape testing for the target-state Control-M orchestration model, covering batch scheduling, dependency validation, restartability, recovery, SLA monitoring, operational handover and end-to-end runbook assurance.
- Align QA practices with regulatory frameworks including SOX, DORA and IFRS, and modern delivery models including Agile, DevOps, DataOps and QA-Ops.
- Partner with business domain experts, data owners, architects and engineering teams to ensure data accuracy, integrity, lineage, financial validation and operational resilience are built into test strategies.
- Design test data and environment strategies for complex financial, integration and analytics scenarios, including payments, GL postings, month-end processes, master/reference data updates, batch orchestration and downstream reporting.
- Drive innovation in testing using AI/ML techniques, performance engineering, observability, service resilience validation and data quality analytics.
- Elevate engineering maturity through shift-left and shift-right testing, reusable automation frameworks, quality gates, continuous improvement and measurable QE outcomes.
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Need to Have - Your Essentials
- Demonstrable experience leading QA or Quality Engineering across modern Data & Analytics platforms, including data ingestion, transformation, orchestration, semantic modelling, reporting and operational support.
- Strong experience testing modern data platform configuration, data feeds, inputs and outputs across Microsoft Fabric, Medallion Architecture patterns, Azure Data Lake Gen2, SQL-based warehouses and related analytics services.
- Practical understanding of Microsoft Fabric workloads including Lakehouse, Warehouse, Data Factory, Dataflows Gen2, notebooks, semantic models, Power BI, DAX and XMLA-based validation.
- Experience validating end-to-end data flows across source systems, integration layers, Lakehouse or warehouse stores, semantic models and downstream reporting outputs.
- Experience or strong working knowledge of EBX, Azure Integration Services, Informatica IDMC and Control-M-style scheduling or orchestration capabilities.
- Proficiency in writing or reviewing technical validation queries and scripts using T-SQL, Python, Power Query, DAX or equivalent technologies for data reconciliation, data quality and transformation testing.
- Experience embedding testing into CI/CD pipelines using Azure DevOps, Git-based workflows, branching strategies, quality gates and automated deployment controls.
- Strong leadership experience in Quality Engineering or QA within enterprise, financial services or regulated environments.
- Proven ability to define test strategy, automation strategy, environment strategy and test data strategy for complex, multi-platform delivery programmes.
- Deep knowledge of test automation frameworks, data reconciliation approaches, interface validation, regression testing, performance validation and service resilience assurance.
- Strong understanding of regulatory and operational risk considerations, including SOX, DORA, IFRS, auditability, traceability and business continuity.
- Ability to influence stakeholders and embed quality across business, architecture, engineering, operations and supplier teams.
- Excellent communication skills, strategic mindset and ability to translate complex technical quality risks into clear business-facing insight.
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Nice to Have
- ISTQB Advanced Test Manager or equivalent quality leadership certification.
- ITIL v3/v4 Foundation or experience aligning QA with service management practices.
- Experience applying AI/ML in Quality Engineering, including test optimisation, defect prediction, anomaly detection or analytics-led assurance.
- Experience with enterprise orchestration tools such as Control-M, Autosys or equivalent scheduling platforms.
- Experience with master or reference data management platforms such as EBX, including governance, workflow and hierarchy validation.
- Experience with enterprise integration observability, API monitoring, data lineage, data governance or data quality tooling.
- Experience supporting Microsoft Fabric adoption, migration or modernisation programmes.
- Familiarity with NIST CSF or other cybersecurity frameworks.
- Experience in performance, scalability and resilience testing using tools such as JMeter, k6 or Locust.
- Experience scaling QE practices across large, complex organisations.
- Familiarity with chaos engineering or resilience testing approaches.
Why You’ll Love This Role
Because this is more than QA leadership.
This is about redefining how quality is engineered across an entire organisation.
You’ll influence how critical financial systems are validated, how risk is reduced, and how engineering teams deliver with confidence at scale. From AI-powered testing to resilience engineering, you’ll be at the forefront of modern QE transformation.
If you want ownership, impact, and the opportunity to build a best-in-class Quality Engineering capability, this is where you make your mark!
Please be advised internal applicants are only eligible to apply once the probation period in your current role has been passed.
Please note that CCTs and applicants working directly for a Board office can only apply for jobs advertised via the external website.
Please note, that due to the high volume of applications received, we regret to inform you that we are unable to provide detailed feedback to candidates who have not been shortlisted (for further consideration).
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