Morgan Stanley's Fraud Technology department is responsible for designing, developing, and maintaining applications that help the firm identify and prevent potential fraudulent transactions. This role aims to create and deliver high-quality, resilient data solutions to Fraud business partners and contribute to the development and maintenance of fraud detection and prevention applications.
Requirements
- Strong with programming in Python
- Batch data engineering on Apache Spark
- Strong with SQL Server & Hadoop based implementations
- Strong SQL skills
- Good hands-on experience with at least one of the job scheduling tools like Autosys (Preferred), Control-M etc.,
- Experience of working in a Linux environment and can write Python/Shell scripts
- Experience in building & maintaining data solutions for BI & Data Science use cases
Responsibilities
- create and deliver high quality, resilient data solutions to our Fraud business partners
- working on various existing and new technology stacks which include on-prem relational and big-data technologies & new cloud-based technologies in AWS and Azure
- share ownership of our projects and contribute to the active development and maintenance of our applications
- be involved in the full development lifecycle
- Batch data engineering on Apache Spark and populate downstream batch data stores (such as data mart) for BI use cases & to generate downstream feeds (ie., flat & wide tables or compressed files) for Data Science use cases
- Real-time service integration to process business events off Kafka and persist in operational MS SQL/MongoDB and/or Neo4j Graph data stores for fraud investigation
- Near real-time stream processing to derive features for ML model inference.
Other
- 5+ years of relevant work experience
- Strong oral and written communication skills
- Excellent interpersonal skills and professional approach
- Strong analytical and problem-solving skills
- Ability to learn quickly and pick up new techniques and/or technologies
