Back-end Engineer - Data Platforms

Morgan Stanley • New York, NY

1d • $155,000 - $215,000

About The Position

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 Back-end Engineer - Data Platforms position at the Vice President level, which is part of the job family responsible for developing and maintaining software solutions that support business needs. 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. We are seeking a highly skilled Vice President Backend Software Developer to design, build, and scale enterprise-grade data platforms and backend services supporting critical business and regulatory workloads. The ideal candidate combines deep technical expertise in distributed data processing, modern data architecture, and system design with strong engineering leadership, code quality, and AI-enabled development practices. As a VP Backend Software Developer, you will be responsible for architecting and delivering scalable backend systems, data pipelines, and platform capabilities that process large volumes of structured and unstructured data. You will partner with product owners, architects, data engineers, and business stakeholders to build resilient, high-performance solutions while establishing engineering best practices across the development lifecycle. This role requires strong expertise in Python, PySpark, Big Data technologies, Snowflake, Graph Technologies, System Architecture, and AI-assisted software development.

Requirements

• Bachelor’s or master’s degree in computer science, Engineering, Information Technology, or related field.
• 8+ years of software engineering experience with a strong focus on backend development.
• Expert-level programming experience using Python.
• Strong hands-on experience with PySpark and distributed data processing.
• Experience with Big Data technologies (e.g., Spark, Hadoop ecosystem, Kafka, Databricks, Delta Lake).
• Strong expertise in Snowflake architecture, data modeling, and performance optimization.
• Experience designing and implementing large-scale data pipelines and data platforms.
• Experience with Graph Technologies and graph-based data solutions.
• Strong understanding of: System Architecture, Application Design Patterns, Microservices Architecture, Event-Driven Architectures, Distributed Systems, API Design.
• Proven experience conducting architecture and code reviews.
• Strong understanding of CI/CD, DevOps, testing frameworks, and software delivery best practices.
• Experience using modern AI developer tools and AI-assisted engineering practices.

Nice To Haves

• Experience within Financial Services, Risk, AML, Transaction Monitoring, Regulatory Technology, or Data Platforms.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Familiarity with containerization technologies (Docker, Kubernetes).
• Experience building data products supporting advanced analytics, machine learning, or AI workloads.
• Knowledge of data governance, metadata management, and data lineage frameworks.

Responsibilities

• Design, develop, and maintain scalable backend applications and services.
• Build high-performance distributed data processing solutions and big data frameworks.
• Develop reusable frameworks, APIs, and platform services supporting enterprise applications.
• Drive design and implementation of real-time and batch processing architectures.
• Design and implement robust data pipelines for ingestion, transformation, validation, and consumption.
• Develop scalable solutions by leveraging Snowflake and modern cloud-based data platforms.
• Optimize large-scale data processing workloads for performance, reliability, and cost efficiency.
• Support data governance, lineage, observability, and data quality initiatives.
• Lead system architecture discussions and define long-term technical roadmaps.
• Produce high-quality architecture and design documentation.
• Evaluate technologies, patterns, and frameworks to improve platform capabilities.
• Ensure solutions meet security, resiliency, scalability, and regulatory requirements.
• Design and implement graph-based data models and analytics solutions.
• Build relationships and network-based insights using graph technologies.
• Evaluate graph databases and related technologies to solve complex business problems.
• Conduct design reviews and code reviews to ensure adherence to engineering standards.
• Establish best practices for testing, CI/CD, performance tuning, and operational excellence.
• Promote software craftsmanship, maintainability, and automation across teams.
• Mentor junior engineers and provide technical leadership.
• Leverage AI-assisted development tools to improve engineering productivity and code quality.
• Drive adoption of AI-powered coding, documentation, testing, and review capabilities.
• Identify opportunities to integrate AI and automation into engineering workflows.

Benefits

• Ample opportunity to move about the business for those who show passion and grit in their work.
• Attractive and comprehensive employee benefits and perks in the industry.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Education Level: Associate degree