Risk Data Engineer

Your key responsibilities

• Data Pipeline Development: Design, develop, and optimize data pipelines for large-scale structured and unstructured datasets.
• Data Handling: Work with SQL for data extraction, transformation, and analysis.
• ETL Workflows: Develop and maintain ETL workflows using PySpark or equivalent technologies.
• Platform Implementation: Implement scalable solutions on Databricks or Snowflake environments.
• Collaboration: Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data products.
• Data Quality and Compliance: Ensure data quality, reliability, and compliance with regulatory standards.
• Domain-Specific Solutions: Contribute to domain-specific solutions for AML, fraud detection, and risk analytics.

Skills and attributes for success

Required Skills:

• 4+ years of experience in data engineering, working with ETL pipelines, SQL, and modern data platforms
• Domain Knowledge: Experience in any 1 or more of these areas
  • AML (Anti Money Laundering) Modelling OR
  • Sanctions screening OR
  • Fraud OR
  • Financial Crime OR
  • Trade Surveillance
• PySpark / Scala Expertise: Hands-on experience with PySpark for data processing and working knowledge of Scala.
• SAS Compliance Packages: Hands-on experience working with SAS Compliance Solutions (AML, FCC, KYC, or Fraud) for data ingestion, data model understanding, or rule execution workflows.
• Modern Data Platforms: Experience working on Databricks
  • Snowflake
• SQL and ETL Development: Strong skills in SQL, data handling, and ETL pipeline development for structured and unstructured data.
• Performance Optimization: Experience with performance optimization and handling large-scale datasets.
• Collaboration: Strong teamwork and communication skills with the ability to work effectively with cross-functional teams.

Preferred Experience:

• Agile Methodologies: Familiarity with Agile development practices and methodologies.
• Problem-Solving: Strong analytical skills with the ability to troubleshoot and resolve complex issues.
• Cloud Platforms: Experience in one of the cloud data platforms (AWS,Azure,GCP)
• CI/CD Practices: Experience with CI/CD practices for data engineering
• Excellent communication and reasoning skills
• GenAI / AI Familiarity: Awareness or training in Generative AI or AI concepts, such as LLMs, prompt engineering, or AI-based data workflows.

Education:

• Degree: Bachelor’s degree in Computer Science, Information Technology, or a related field, or equivalent practical experience