Data Solution Architect - Risk & Financial Crime Analytics

About the role

A global banking and financial services group is looking for a Data Solution Architect to design and deliver scalable enterprise data and advanced analytics solutions across the full software development lifecycle. The role drives end-to-end delivery of large-scale analytics platforms, turning complex business and regulatory needs into robust architectural designs.

Key responsibilities

• Design and deliver scalable enterprise data and advanced analytics solutions across the complete software development lifecycle.
• Drive end-to-end delivery of large-scale analytics platforms, translating business and regulatory needs into architectural designs.
• Review, refine and approve testing strategies, validation controls and reconciliation frameworks to protect platform integrity.
• Evaluate emerging data and analytics tooling and collaborate on proof-of-concept initiatives to advance enterprise modernization.
• Formulate technical architectures supporting natural language processing, entity resolution rules and relationship-driven network analytics.
• Partner with technical delivery and support teams to resolve incidents, perform root cause analysis and strengthen data quality controls.

Required profile

• Substantial experience delivering enterprise systems analysis and modern data architecture in corporate banking, financial crime or credit risk domains.
• Strong technical background designing data pipelines that ingest and govern structured, semi-structured and unstructured text datasets for natural language processing applications.
• Proven understanding of data governance, data lineage, fine-grained access security and regulatory data management standards in banking environments.

Required skills

• Distributed processing and query engines such as Spark, Trino, Presto or Hive, and open table formats such as Apache Iceberg, Delta Lake or Apache Hudi.
• SQL, BTEQ, Python and enterprise data integration tools such as Informatica PowerCenter or Big Data Management.
• Data pipelines, ETL/ELT, data governance, quality and lineage, knowledge graphs and relationship analytics.