Data Engineer -- Regulatory Reporting & Portfolio Intelligence

About the Role:

Join our Risk & Compliance team as a Data Engineer supporting Regulatory Reporting and Portfolio Intelligence initiatives. You will work closely with Country Compliance, Money Laundering Reporting Officers (MLROs), Risk, and Product teams to build the data infrastructure that powers regulatory compliance, reporting, and portfolio management across Nium's global operations.

This is a technical role with a strong regulatory edge - you'll not only architect and implement scalable data pipelines and connectors, but also help translate complex regulatory requirements into actionable data solutions. You'll be a bridge between compliance/risk domain expertise and technical data engineering, ensuring our regulatory data is accurate, auditable, and readily available for both internal decision-making and regulatory submissions.

Key aspects of the role:

• Design and build scalable, maintainable data pipelines for regulatory and compliance data
• Develop and maintain XML and REST API connectors to ingest data from banking partners, regulatory bodies, and internal systems
• Implement bit request processing for real-time transaction data integration and portfolio updates
• Interpret regulatory requirements (transaction reporting, sanctions screening, KYC/AML, portfolio limits) and translate them into data models and processing logic
• Work directly with Country Compliance, MLRO, Risk, and Product teams to understand data needs and operationalize compliance rules
• Ensure all data flows are auditable, traceable, and compliant with regulatory standards and internal policies

Key Responsibilities:

• Build and optimize scalable, maintainable, and high-performance data pipelines for regulatory data ingestion, transformation, and delivery.
• Develop XML and REST API connectors to integrate data from banking partners, regulatory feeds, internal systems, and risk/compliance platforms.
• Implement bit request handlers to support real-time transaction and portfolio data updates for risk monitoring and regulatory reporting.
• Work with Compliance, MLRO, and Risk teams to understand regulatory filing and reporting requirements, then design data models and transformations to support them.
• Interpret regulatory requirements (sanctions lists, transaction thresholds, portfolio limits, reporting deadlines) and encode them into data logic and quality checks.
• Design and maintain efficient data models in cloud data warehousing solutions (e.g., Redshift) optimized for regulatory reporting, audit trail queries, and risk analytics.
• Write complex SQL queries and transformations to prepare data for compliance analysis, regulatory submissions, and audit trails.
• Ingest and transform both structured (databases, APIs) and semi-structured (XML, JSON) data at scale from diverse compliance and risk sources.
• Establish robust data quality checks, monitoring, and alerting mechanisms to ensure data integrity for regulatory compliance and risk control effectiveness.
• Support cloud migration initiatives and modernize legacy data pipelines (e.g., on premises to AWS).
• Document data pipelines, connectors, and technical architecture for auditability, regulatory inspection readiness, and cross-team knowledge sharing.
• Collaborate with Country Compliance, MLRO, Risk, Product, and Data teams to operationalize compliance rules and regulatory logic into scalable systems.
• Evaluate effectiveness of data controls, monitoring, and quality checks using testing and validation (e.g., completeness checks, SLA validation, rule effectiveness).

Requirements:

• 3-5 years of professional experience in Data Engineering, ETL/ELT pipeline development, or systems integration
• Strong SQL expertise including query optimization, complex transformations, and data modelling.
• Solid proficiency in Python (or similar language) for writing data pipelines, scripts, and data processing logic.
• Hands-on experience designing and developing API connectors (REST, SOAP) and working with XML/JSON data formats.
• Experience with cloud data platforms (AWS Redshift, Snowflake, Google Big Query, or similar) and cloud storage (S3, GCS, etc.)
• Familiarity with data orchestration tools (Apache Airflow, Prefect, or equivalent) for workflow automation.
• Understanding of data modelling concepts and ability to design efficient schemas for analytical and operational workloads.
• Experience with real-time or near-real-time data processing (Kafka, streaming pipelines) is a plus.
• Knowledge of data quality frameworks and testing practices for data pipelines.
• Strong problem-solving skills and ability to debug complex data integration issues.
• Collaborative mindset with ability to work across technical and non-technical stakeholders.
• Familiarity with regulatory reporting requirements (e.g., transaction reporting, sanctions screening, KYC/AML, portfolio monitoring) is highly valuable.
• Ability to learn and interpret regulatory documentation and translate compliance/risk requirements into technical specifications.