Data Engineer (Snowflake)_Associate_Software Engineering

Profile Description Division We are looking for a Data Engineer (3–5 years) with strong Python programming skills and hands-on experience building modern data platforms using Snowflake, dbt, and Apache Airflow. You will design and develop reliable data pipelines, model and transform data for analytics, and collaborate using Git-based workflows to deliver high-quality, production-ready data solutions. Experience with Power BI and exposure to AI/ML use cases is a plus. Investment_Management In the Investment Management division, we deliver active investment strategies across public and private markets and custom solutions to institutional and individual investors. IMIT App Dev Operations Technology is responsible for building and operating the technology platform catering to critical business processes at Morgan Stanley including Settlements, Confirmations, Regulatory Reporting, Position Keeping, Corporate Actions processing and other post-trade functions. The Firm operates at scale with up to 60 million trades processed on peak volume days and several trillion dollars of daily settlements with activity ongoing in multiple countries and currencies across the globe. There is a great breadth of financial products that our Operations plant handles across equity and fixed income, from cash products to complex derivatives and loans. Software Engineering The Lead will get involved in the design, development, and testing of multi-tier systems and components across the platforms 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. At Morgan Stanley India, we support the Firm’s global businesses, with critical presence across Institutional Securities, Wealth Management, and Investment management, as well as in the Firm’s infrastructure functions of Technology, Operations, Finance, Risk Management, Legal and Corporate & Enterprise Services. Morgan Stanley has been rooted in India since 1993, with campuses in both Mumbai and Bengaluru. We empower our multi-faceted and talented teams to advance their careers and make a global impact on the business. For those who show passion and grit in their work, there’s ample opportunity to move across the businesses for those who show passion and grit in their work. Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on… What You’ll Do In The Role Design, build, and maintain scalable ETL/ELT pipelines using Apache Airflow (DAG design, scheduling, monitoring, retries, alerting). Develop and optimize cloud data warehousing solutions on Snowflake (schemas, performance tuning, clustering/partitioning strategies where applicable, cost awareness). Implement analytics engineering best practices using dbt: Data modeling (star/snowflake schemas, dimensional modeling) Reusable macros, tests, documentation, and lineage Environment management (dev/test/prod) Write clean, efficient, and maintainable Python code for data ingestion, transformation, orchestration, and automation tasks. Ensure data quality and reliability through validation, automated testing, and observability/monitoring. Use Git-based workflows (branching, PRs, code reviews) and contribute to CI/CD practices for data pipelines and dbt projects. Troubleshoot pipeline failures, resolve data issues, and continuously improve performance and stability. What You’ll Bring To The Role 3–5 years of professional experience in Data Engineering or related roles. Hands-on experience with Snowflake, dbt, and Apache Airflow in production or enterprise environments. Strong programming skills in Python (data processing, APIs, automation, packaging, best practices). Solid understanding of data engineering concepts: ETL/ELT patterns, orchestration, batch processing (and streaming familiarity is a plus) Data modeling, warehousing concepts, and SQL optimization Strong SQL skills and experience working with large datasets. Working knowledge of Git-based development workflows (feature branches, pull requests, reviews, merging strategies). Good To Have (Preferred Skills) Experience building dashboards or semantic models with Power BI. Exposure to AI/ML concepts or enabling AI use cases (feature datasets, data prep for model training/inference, LLM/AI pipeline awareness). Kafka (integration / streaming support) Great Expectations (data quality checks / validation framework) Soft Skills Strong problem-solving and debugging abilities. Good communication skills and ability to work cross-functionally. Ownership mindset with attention to data quality, reliability, and documentation. 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