Description:

Morgan Stanley is looking for a Data warehousing Engineer (Director – Data & Analytics Engineering) to join its growing Cybersecurity and Technology Risk team in Bengaluru.

About the Organization:

The Cybersecurity division at Morgan Stanley is focused on defending against advanced cyber threats and maintaining resilience across systems and data. Within this, the Tech Risk Gov & Controls (TRGC) team oversees governance and ensures risks are identified, mitigated, and managed across technology.

Role Overview:

As a Datawarehousing Engineer at the Director level, you’ll be responsible for building scalable data pipelines, managing data transformations, ensuring high data quality, and enabling analytics and reporting for business-critical insights. You’ll work across modern data platforms and contribute to designing systems that support decision-making and risk governance at scale.

Key Responsibilities:

• Design and develop scalable, high-performance data pipelines.
• Transform and model data for analytics, reporting, and governance needs.
• Ensure data integrity, accuracy, and availability across systems.
• Collaborate with teams to implement Snowflake-based solutions, optimize performance, and manage access controls.
• Monitor pipeline health, troubleshoot issues, and enforce best practices in data quality.
• Utilize workflow orchestration tools like Airflow or Dagster for automation.
• Implement and optimize ETL processes using dbt and Python.
• Support the design and implementation of REST APIs for data services.
• Collaborate with cross-functional teams in a fast-paced, Agile environment.

Key Technical Skills:

Python, Apache Spark (PySpark), Snowflake, NoSQL, Graph Database, dbt, Airflow/Dagster, SQL, Data Modeling, Git, CI/CD, REST APIs

Requirements:

• At least 4+ years of strong hands-on experience as a Data Engineer (Director-level candidates will typically bring 10+ years of overall experience).
• Proficiency in Python for scripting, ETL, and data manipulation.
• Expertise with Apache Spark or equivalent big data frameworks.
• Solid experience with Snowflake, including schema design, optimization, and warehouse management.
• Strong SQL and data modeling knowledge.
• Hands-on experience with dbt for transformations and data orchestration.
• Familiarity with Airflow or similar workflow tools.
• Strong communication, collaboration, and problem-solving skills.
• Experience working with version control and CI/CD practices.