Consultant Conversion - Data & Analytics Eng II - Associate

In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Data & Analytics Engineering position at the Associate level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques.

Morgan Stanley is an industry leader in financial services, known for mobilizing capital to help governments, corporations, institutions, and individuals around the world achieve their financial goals.

Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on.

Non-Financial Risk Technology provides operational controls and surveillance capabilities to enhance the firm’s resilience to threats and fraudulent behavior.

The Fraud Technology department is responsible for designing, developing, and maintaining applications, which helps the firm identify and prevent potential fraudulent transactions. We also provide technology expertise to our fraud analysts in operations.

What you'll do in the role:

As a senor data engineer your role will be to create and deliver high quality, resilient data solutions to our Fraud business partners and being a productive member of the development team. You will be 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. You will be expected to share ownership of our projects and contribute to the active development and maintenance of our applications. You will have the opportunity to be exposed to modern software engineering tools and best practices.  You will have the opportunity to be exposed to how a large investment bank like Morgan Stanley detects and prevents fraud.  You will work in a dynamic agile team that uses Scrum for its workflow. You will be expected to be involved in the full development lifecycle. You will be expected to collaborate with others in the wider team as well as working on your own initiative.

What you'll bring to the role:

• 5+ years of relevant work experience

• Strong with programming in Python to perform, 

  • 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

• 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

• Strong data analytics skills

• 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

• Experience in building & maintaining data solutions for BI & Data Science use cases

Skills Desired

• Experience in data architecture and modeling experience. Especially, in dimensional modeling for data mart design and development to support BI 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. 

• Experience in Azure (Databricks, Data Factory, Synapse, Azure Data Lake) and/or AWS (AWS S3, AWS Athena, AWS Glue) Data ecosystem & Snowflake Data Cloud

• Experience in MongoDB and Neo4j Graph databases

• Experience in building virtual data access layer using TIBCO Data Virtualization to support BI & Data Science use cases

• Experience of the full software development life cycle

• Experience of working in an Agile team

• Experience of working with version control systems

• Experience with bash scripting

• Experience of working with Continuous Integration systems

• Experience in Fraud detection and prevention business in Financial Services