I'm looking for a technically strong Financial Crime Data Lead to take ownership of the data engineering capability supporting financial crime operations within a leading UK financial services organisation.
Deep, hands-on expertise in both Snowflake and dbt is absolutely essential for this position.
This isn't a purely strategic or people management role. I'm looking for someone who has significant experience designing, building and optimising production data platforms using both technologies, and who can set the technical direction whilst remaining close to the engineering.
The role offers the opportunity to build a trusted financial crime data foundation, bringing together information across transaction monitoring, screening, onboarding, CRM and operational systems to improve data quality, regulatory controls, reporting and future AI capabilities
The role
You'll take technical ownership of the financial crime data platform, shaping its architecture, engineering standards and delivery whilst leading a focused data engineering capability.
Key responsibilities include:
- Snowflake architecture and engineering: Own the design, implementation and optimisation of Snowflake environments, including warehouse architecture, schema design, performance tuning, security, access controls and cost optimisation.
- dbt development and architecture: Establish and develop robust dbt transformation frameworks, including complex SQL models, incremental processing, macros, testing, documentation, lineage and CI/CD integration.
- Hands-on data engineering: Design and build production-grade SQL and Python pipelines, integrating data from multiple internal and external platforms.
- Data modelling: Develop scalable data models covering clients, entities, transactions, alerts, screening outcomes, risk ratings, investigations and audit records.
- Data quality and governance: Establish automated testing, reconciliation, data lineage, monitoring and governance controls across sensitive financial crime datasets.
- Technical leadership: Define engineering standards, review architectural decisions, mentor engineers and ensure delivery follows strong software engineering practices.
- Financial crime integration: Bring together data from AML, KYC, transaction monitoring, screening, CRM and other operational systems.
- AI and automation: Build reliable, well-governed datasets that support future machine learning, anomaly detection, alert optimisation and investigation automation.
What I'm looking for
The most important requirement is someone with genuine technical depth across Snowflake and dbt, backed by strong SQL/Python engineering and experience leading data platform delivery.
Essential experience:
- Extensive, hands-on Snowflake engineering experience, including architecture, data modelling, performance optimisation, warehouse management, security and cost control.
- Advanced dbt expertise, having designed and developed production transformation frameworks rather than simply worked with existing models.
- Strong SQL skills, including complex transformations, query optimisation, data quality checks, reconciliation and scalable modelling techniques.
- Strong Python engineering capabilities for pipelines, automation, API integrations, testing and reusable data processing.
- Experience designing and delivering modern cloud data platforms in complex enterprise environments.
- Strong understanding of data engineering best practices, including Git, CI/CD, automated testing, observability and production support.
- Previous experience leading engineers, setting technical standards and taking ownership of architecture and delivery.
- Experience working with sensitive, governed data in financial services or another highly regulated environment.
Particularly interesting experience:
- Financial crime, AML, KYC, sanctions screening or transaction monitoring data.
- Integrating financial crime platforms, CRM systems and third-party vendor APIs.
- Data Vault, dimensional modelling or complex entity resolution.
- Building data foundations for machine learning, anomaly detection or GenAI applications.
- Experience with Azure Data Factory, Airflow or similar orchestration technologies.
Why consider it?
This is an opportunity to take genuine technical ownership of an important data engineering capability, rather than inherit an established platform with limited scope to influence its direction.
You'll help shape how financial crime data is structured, integrated, governed and consumed across the organisation, with the opportunity to introduce engineering standards, improve operational processes and enable future automation and AI use cases.
The role offers a combination of hands-on technical challenges, architectural ownership, leadership responsibilities and direct exposure to senior business and technology stakeholders.
Package:
Up to £120,000 base salary, plus bonus, excellent pension and wider benefits.
Location:
London, Glasgow or Liverpool, with hybrid working. London-based employees attend the office 2–3 days per week.
If you're a senior Data Engineer, Principal Data Engineer or Data Engineering Lead with substantial Snowflake and dbt experience, and you're interested in taking ownership of a business-critical data platform, I'd be keen to have a conversation.
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