Senior Data Analyst KYC Consultant

h3Mission Overview: /h3 pKeystone Solutions is looking for a Senior Data Analyst with strong KYC expertise for a consultancy mission in Brussels. You will be hired by Keystone Solutions and represent us while working on a client project under our consultancy model. The mission combines 50% on-site work with 50% homeworking. /p pAs a Data Analyst, you will play a critical role in designing, delivering, and maintaining high-quality data solutions across the KYC lifecycle, covering customer onboarding, periodic reviews, and event-driven updates. You will support compliance, operational efficiency, and scalability by making customer identification, due diligence, risk assessment, and KYC process data reliable and usable. You will act as a bridge between data providers such as IT, Data Services, AI, and Operations and data consumers such as KYC Operations, Front Office, Compliance teams, and regulators, aligning technical solutions with business needs while upholding strict data governance and quality standards. /p br/ h3Core

Responsibilities:
/h3 pThese responsibilities will be carried out as a Keystone Solutions consultant within the client environment. /p ul libStakeholder Alignment and Communication: /b Coordinate input from IT, Data Services, Compliance (CPL), KYC Operations, and Front Office, ensuring alignment on KYC data needs through structured documentation, workshops, and clear communication. /li liTranslate KYC business requirements for onboarding, reviews, customer due diligence, and risk assessment into actionable data solutions, balancing technical feasibility with regulatory and operational needs. /li libData Governance and Quality: /b Establish and enforce data governance frameworks, including role definitions, quality rules, and access protocols, in collaboration with data providers and consumers. /li liMonitor, refine, and elevate data quality issues proactively, ensuring long-term integrity of KYC datasets, including customer identification, legal entity and beneficial ownership information, related parties, risk classifications, and due diligence records. /li liDefine and document business and technical rules for KYC data integration, including source-to-target mappings, data lineage, and reconciliation controls, ensuring traceability and compliance. /li libData Analysis and Troubleshooting: /b Investigate and resolve discrepancies across customer reference data, KYC applications, and mainframe-based data points. A willingness to explore raw data is essential. /li liWrite and run SQL queries to extract, join, validate, and analyze KYC data across source systems, uncovering trends, data quality issues, and actionable insights. /li liAnalyze KYC customer, risk, screening, and process data to identify missing or inconsistent information, coverage gaps, and operational bottlenecks across onboarding and reviews. /li libSolution Design and Industrialization: /b Design logical and physical data models, such as data warehouses and datamarts, optimized for KYC reporting and analytics, linking customers, legal entities, beneficial owners, related parties, risk assessments, and process history. /li liDevelop and industrialize data pipelines, ensuring reliable flows between KYC source systems, shared data platforms, and reporting solutions, with appropriate accessibility across the enterprise. /li liBuild and maintain interactive dashboards to visualize KYC data and KPIs, including onboarding and review volumes, risk distribution, cycle times, waiting times, and handovers, preferably using Power BI. Translate findings into actionable insights to support business decisions and process improvements. /li liPrototype KYC data solutions and dashboards to validate business requirements and refine designs based on feedback. /li libProcess Optimization: /b Streamline KYC data workflows to maximize reusability and efficiency, reducing duplicate data collection and manual checks. Use process history to measure cycle times, waiting times, handovers, and rework, and identify opportunities for process improvement. /li liAdvocate for best practices in data security, architecture, and compliance, aligning with IT and risk management standards. /li /ul br/ h3Language

Requirements:
/h3 ul liStrong written and spoken communication skills in Dutch or French. /li liStrong written and spoken communication skills in English. /li liExcellent communication skills, with fluency in Dutch, French, and English, to engage technical and non-technical stakeholders. /li /ul br/ h3Education: /h3 ul liBachelor's or Master's degree in a quantitative discipline such as statistics, mathematics, or engineering. /li liCandidates with other academic backgrounds and substantial knowledge of analytics are also encouraged to apply. /li /ul br/ h3Required Experience and Technical Knowledge: /h3 ul liAt least 5 years of relevant experience in Data Analytics. /li libMandatory: /b 2–6 years of hands-on experience in Data Analytics, with a focus o