Data Engineer - Director - Data & Analytics Engineering at Morgan Stanley

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

25 Mar, 26

Salary

0.0

Posted On

25 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Apache Spark, Kafka, MS SQL, Neo4j, Hadoop, SQL, Stored Procedures, Linux, Shell Scripting, Data Architecture, Dimensional Modelling, Data Analytics, Machine Learning, AI Techniques, Agile

Industry

Financial Services

Description

We’re seeking someone to join our CDRR Technology team as a Data Engineer, in Non-Financial Risk Technology to which provides operational controls and surveillance capabilities to enhance the firm’s resilience to threats and fraudulent behaviour. 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. 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 Director position that provides specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques. Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world. What you’ll do in the role: As a senior 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: 6 to 8 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 Real-time service integration to process business events off Kafka and persist in operational MS SQL and/or Neo4j Graph data stores for fraud investigation Near real-time stream processing to derive features for ML model inference. Strong with SQL Server & Hadoop based implementations Strong SQL & Stored Procedure 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 architecture and modelling experience. Especially, in dimensional modelling for data mart design and development to support BI use cases 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 WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work. To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser. Morgan Stanley is an equal opportunities employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. Our skilled and creative workforce is comprised of individuals drawn from a broad cross section of the global communities in which we operate and who reflect a variety of backgrounds, talents, perspectives, and experiences. Our strong commitment to a culture of inclusion is evident through our constant focus on recruiting, developing, and advancing individuals based on their skills and talents.

Responsibilities

As a senior data engineer, you will create and deliver high-quality data solutions for the Fraud business partners while being an active member of the development team. You will work on various technology stacks, including on-premises and cloud-based technologies.