Financial Crime Data Lead

Role Title: Financial Crime Data Lead

Division: Digital & Functions Technology

Location: Glasgow/Liverpool/London

Contract: Perm

About the Role

The Financial Crime Data Lead is a senior data leadership role with accountability for shaping, building and continuously improving the data foundations that underpin Rathbones’ financial crime capability.

The role is central to creating a trusted financial crime data layer that integrates transaction monitoring, client screening, onboarding, client risk, mandate and CRM data into a single usable foundation for workflow, MI, controls, analytics and future automation.

The role will suit an experienced data leader who can combine SQL, Python, Snowflake, data engineering, AI enablement, data governance and financial crime knowledge to reduce operational risk, improve decisioning and enable a single view of financial crime activity across clients and entities.

What you’ll be responsible for

• Own the design and delivery of the financial crime data layer, bringing together relevant data from ComplyAdvantage, OCS, Salesforce, onboarding, mandate and enterprise data sources.
• Define the canonical financial crime data model covering clients, entities, relationships, alerts, screening outcomes, transactions, risk ratings, cases, decisions, source evidence and audit events.
• Lead data engineering across ingestion, transformation, matching, validation, lineage, quality monitoring and controlled publication into downstream workflow, MI, UX and analytics layers.
• Establish SQL and Python engineering standards for reusable pipelines, data testing, reconciliation, automation, exception handling and maintainable production-grade data products.
• Own Snowflake design patterns for financial crime data, including schemas, role-based access, data sharing, performance optimisation, data lifecycle management and secure analytical consumption.
• Develop operational MI and data products that provide clear visibility of alert volumes, case status, backlog, outcomes, control performance and emerging risk indicators.
• Create the foundations for future AI and automation use cases, including explainable alert optimisation, investigation summaries, anomaly detection, adverse media triage and proactive risk insights.
• Partner with Architecture, Financial Crime SMEs, Risk, Compliance, Operations, Salesforce teams and data platform teams to ensure the data layer supports end-to-end business journeys.
• Define data ownership, stewardship and governance arrangements for financial crime data, including controls over sensitive data, retention, access, usage and auditability.
• Build and lead a focused financial crime data engineering capability, fostering strong engineering discipline, documentation, ownership, code quality and continuous improvement.

About you

If you meet some of these criteria and are excited about the role, we encourage you to apply

• Significant experience leading data engineering, data platform or data product delivery in complex, regulated financial services environments.
• Strong hands-on SQL capability, with experience designing performant queries, reusable data models, analytical marts, reconciliation logic, data quality checks and production-grade transformations.
• Strong Python capability for data engineering and automation, including pandas or PySpark-style transformations, API integration, data validation, orchestration support, testing and reusable code patterns.
• Strong experience with Snowflake or equivalent modern cloud data platforms, including warehouse design, schema modelling, performance tuning, secure data sharing, role-based access and cost-conscious usage.
• Experience integrating data from CRM, workflow, risk, compliance, screening, monitoring and third-party vendor platforms using APIs, files, batch and event-based patterns.
• Good understanding of financial crime data domains, including clients, beneficial owners, transactions, screening results, sanctions/adverse media matches, risk ratings, alerts and case outcomes.
• Practical appreciation of the data challenges that drive false positives, manual remediation, duplicate records, inconsistent workflows and weak auditability.
• Strong understanding of data privacy, information security, regulatory traceability and access controls in sensitive financial crime use cases.
• Strong communication skills, able to translate complex data issues into decisions, priorities and pragmatic delivery plans.
• Proven ability to build capability, set standards and lead teams with pace, ownership, quality and continuous improvement.
• Technical Skills & Engineering Requirements
• Advanced SQL engineering skills, including dimensional and Data Vault-style modelling awareness, query optimisation, window functions, data reconciliation, exception reporting and automated data quality checks.
• Advanced Python skills for data pipelines, API integration, automation and analytical prototyping, with good use of version control, packaging, testing, logging and secure coding practices.
• Strong Snowflake engineering knowledge, including warehouses, databases, schemas, streams, tasks, stages, secure views, masking policies, role-based access, clustering considerations and performance/cost monitoring.
• Experience with orchestration and pipeline tooling such as Azure Data Factory, dbt, Airflow, Dagster or similar, with the ability to define reliable, observable and recoverable data flows.
• Experience with CI/CD and DevOps for data products, including Git branching, pull requests, automated tests, deployment pipelines, environment promotion and rollback controls.
• Knowledge of data quality and observability practices, including source-to-target reconciliation, data contracts, freshness checks, completeness checks, anomaly detection and operational alerting.
• Experience preparing AI-ready datasets, including feature engineering, labelling, training/validation splits, bias checks, explainability inputs and controlled publication of model features.
• Working understanding of machine learning approaches relevant to financial crime, including anomaly detection, entity resolution, network analysis, classification, clustering and alert prioritisation.
• Awareness of GenAI enablement patterns, including retrieval-augmented generation, vector stores, embeddings, secure grounding, prompt evaluation, human review controls and protection of sensitive data.
• Ability to engineer data foundations that support model governance, audit trails, explainability, lineage, monitoring and responsible AI controls in a regulated environment.

Measures of Success

• A trusted financial crime data layer that brings together key monitoring, screening, onboarding, mandate and CRM data sources.
• Clear canonical data model, ownership, lineage and control framework for financial crime data.
• Improved data quality with fewer manual workarounds, reconciliations and operational exceptions.
• Reduced false positives and clearer operational visibility through better data, MI and outcome tracking.
• Reusable SQL, Python and Snowflake engineering patterns that support workflow, UX, reporting, analytics and future automation.
• AI-ready data foundations with appropriate governance, explainability, security and human-in-the-loop controls.
• A high-performing data engineering capability with clear standards, documentation, resilience and delivery discipline.