Data Quality Manager

Purpose of the Job

Quintet Private Bank is a leading private bank in the wealth management sector; we are committed to our clients and their families, and pride ourselves on our personalised service based on a deep understanding of what clients want to achieve. We are a bank headquartered in Luxembourg with an ambition to stay true to our purpose to be the most trusted fiduciary of family wealth. 

When you join Quintet you are joining a company that values diversity of background, equal access to opportunities, career development, collaboration and inclusiveness. We want our employees to feel proud of being part of a company that is committed to do the right thing. You will have the opportunity to grow your career while developing personally and professionally through various resources and programmes.

Are you driven by building trusted, decision-grade data in a regulated environment? Do you want to directly influence how data underpins client outcomes, risk management, and regulatory compliance in wealth management?
We are hiring a Senior Data Quality Specialist to lead the design, implementation, and continuous improvement of our group data quality capability. This role is critical to ensuring our data is fit for purpose, controlled, measurable, and trusted across investment, client, risk, and finance domains.
You will operate at the intersection of data governance, data engineering, and business ownership, embedding data quality into processes not as an afterthought. You will partner with Data Owners, Data Stewards, Technology, and Risk to ensure our data meets internal standards and external regulatory expectations. 
If you are passionate and motivated to build and scale a robust data quality capability and ensure trusted, decision-grade data across the organisation, we want to hear from you!

Key Accountabilities

• Group Data Quality Framework: Continuously enhance the bank data quality framework in alignment with data governance policies by defining and maintaining data quality dimensions, rules, thresholds, and KPIs (accuracy, completeness, timeliness, consistency, validity, uniqueness), and by establishing critical data elements with clear ownership and controls.


• Data Quality Monitoring & Controls: Implement automated data quality controls using Collibra and integrated data platforms, perform and operationalise data profiling across critical datasets to identify anomalies, patterns, and data defects, build data quality dashboards and scorecards for executive and domain-level visibility, and ensure root cause analysis and remediation workflows are embedded and tracked.  


• Regulatory & Risk Alignment: Ensure data quality practices support regulatory obligations, client reporting, and financial disclosures, partner with Risk and Compliance to ensure auditability, lineage, and control evidence, and lead responses to internal audit and regulatory reviews relating to data quality.                    


• Data Quality and Accessibility : Ensure business teams have access to accurate, high-quality data for decision-making and monitor and improve data integrity across systems and processes.


• Data Issue Management & Remediation: Establish and operate a data issue management lifecycle covering identification, triage, remediation, closure, and prevention, drive business ownership of data quality issues with clear SLAs and accountability and ensure systemic fixes rather than manual patches.                            


• Data Ownership & Stewardship: Embed data ownership and stewardship models across domains, coach and support Data Stewards in defining and maintaining data quality rules, and drive data literacy and awareness across business and technology teams.                    


• Integration with Data Lifecycle: Embed data quality controls into data pipelines, onboarding processes, and transformation layers, work closely with Data Engineering to ensure quality by design, and ensure new use cases and products include data quality controls from inception

Knowledge and Experience

• Further professional Education (Masters / Professional qualification)    


• More than 5 years experience in a similar role 

Attributes and Qualities

• Client-Centric Mindset                            


• Communication (Verbal & Written)                    


• Adaptability & Learning Agility                        


• Critical Thinking & Problem Solving                    


• Collaborative Teamwork

Technical Skills

• Experience with data profiling, data lineage, and metadata management and ability to translate business requirements into implementable data quality rules.                                


• Strong understanding of data Quality frameworks, methodologies, and standards (e.g., DCAM, DAMA).                        


• Experience with Collibra or similar tools: Demonstrated expertise in successfully implementing data quality in Collibra or comparable platforms, including proficiency in handling workflows, data dictionaries, business glossaries.


• Proven track record of supporting data-driven transformations in complex environments.                                


• Strong knowledge of with data technologies like SQL, Python, ETL tools, Apache Spark, and modern API frameworks.                


• Strong understanding of data modeling, metadata management, and data integration.

Languages Skills

• Fluent in English, French would be a plus