Senior Data Engineer – AML Data Platforms & Pipelines

Saxo Bank; København; On-site; DKK 900,000 - 1,300,000; Full time

Job summary

Saxo Bank in Copenhagen, Denmark seeks a Senior Software/Data Engineer to join the FCP Modelling & Development team. You will build and improve data products, design robust ETL pipelines, and ensure secure data exchange across platforms while aligning with AML requirements.

You will collaborate with FCP/AML experts on risk rating, monitoring, and data governance, deploying production-ready systems with strong security and observability practices.

Qualifications

• 5+ years of experience in data engineering, software engineering, data-platform engineering, or a related production data role.
• Practical experience with modern data platforms such as Databricks, Kafka, Snowflake, Airflow, Azure, or similar technologies.
• Experience supporting ML workflows, including feature engineering, feature data preparation, dataset versioning, reproducible training and validation datasets, model-validation support, and production-ready model inputs.
• Deployment skills for production workloads, including CI/CD, containerized services, and Kubernetes or similar orchestration platforms.
• Strong understanding of authentication, authorization, data access controls, encryption, secure handling of sensitive data, and practical data-security patterns using Azure services such as Microsoft Entra ID, Azure Key Vault, Azure RBAC, and Managed Identities.
• Hands-on experience maintaining production data pipelines, including data quality testing, reconciliation, lineage, metadata management, documentation, monitoring, and operational controls.
• Experience with financial crime prevention in regulated financial services, including AML, KYC/CDD/ODD, sanctions screening, transaction monitoring, client risk rating, and market-abuse surveillance support.
• Experience with GDPR-sensitive data, audit evidence, regulator-facing reporting, or data governance in control-heavy environments.

Responsibilities

• Build, operate, and continuously improve reusable AML data products, data-modelling components, and user-facing interfaces, with clear ownership, lineage, documentation, quality controls, and production reliability.
• Design robust ETL pipelines and integration interfaces for operational, analytical, model-development, and external-platform use cases, enabling secure and efficient data exchange across internal and external systems.
• Translate FCP and AML requirements into durable, adaptable production systems, contributing to solution architecture decisions on design patterns, system scalability, regulatory compliance, and flexibility across AML processes.
• Partner with FCP and AML subject-matter experts on transaction monitoring, customer segmentation, scenario development, client risk rating, surveillance support, and AML-related development initiatives that strengthen financial crime prevention capabilities.
• Define automated checks for completeness, consistency, validity, reconciliation, source-to-target integrity, architectural integrity, security, performance, and governance standards.
• Own observability, alerting, incident response, backfills, runbooks, performance tuning, production support, and operational resilience for critical data assets.
• Raise engineering standards through design and code reviews, mentoring, reusable patterns, clean and well-documented code, test-driven development, secure coding practices, CI/CD pipeline optimization, automated testing, and continuous improvement.

Skills

Python

SQL

Spark

Tools

Databricks

Kafka

Snowflake

Airflow

Azure

Job description

Saxo Bank in Copenhagen, Denmark seeks a Senior Software/Data Engineer to join the FCP Modelling & Development team. You will build and improve data products, design robust ETL pipelines, and ensure secure data exchange across platforms while aligning with AML requirements.

You will collaborate with FCP/AML experts on risk rating, monitoring, and data governance, deploying production-ready systems with strong security and observability practices.