Dir, P4, Lead Tech Product Owner : Job Level - Director

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 Lead Data & Analytics Engineering position at the Director 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.

AIDT under Wealth Management Technology provides consistent, trusted, and integrated business information across all platforms and delivery channels within Morgan Stanley Wealth Management (MSWM) businesses. AIDT areas of expertise include but not limited to Enterprise Data Warehouse (EDW) and Datalake (DL), cross-business-unit Information Management, Business Intelligence, AI/ML and more. AIDT team synthesizes and provisions a multitude of business-critical metrics and underlying detailed information in all Core areas of MSWM. 

As an integral part of AIDT, Data Visualization team is tasked with providing insightful and actionable business intelligence solutions for various business unit under wealth management. Examples of essential data domains and metrics include Assets, Revenue, Account Demographics, Financial Advisor (FA) information, Securities and Cash Transactions, Risk, Stock Plan, Retirement, Financial Wellness, and other key areas. The candidate will work on either one or many visualization projects, and the role requires active participation with leadership teams, business units and technology groups across the project lifecycle. Specific assignments will depend on the size and complexity of the project.

What you’ll do in the role:

• Develop and build enterprise level Full Stack applications using BI and ETL technologies.

• Design and develop scalable data model to create intuitive reports and dashboards.

• Design and Development of ETL/Hadoop, including stored procedures, queries, performance tuning, archiving, etc., using python, SQL and ETL tools.

• Development of new transformation processes to load data from source to target, or performance tuning of existing ETL code (mappings, sessions) and Hadoop Platform.

• Build data pipelines and efficient automation scripts (using Python)

• Undertaking end-to-end project delivery (from inception to post-implementation support), including review and finalization of business requirements, creation of functional specifications and/or system designs, and ensuring that end-solution meets business needs and expectations.

• Analysis of existing designs and interfaces and applying design modifications or enhancements.

• Providing insights and analysis findings for ad-hoc issues.

What you’ll bring to the role:

• 5+ Years of Development creating End to End Full Stack BI Solution, using BI Tools (Power BI, Tableau, Business Objects) and ETL Development (Informatica 

• 5+ years of experience with the technical analysis and design, development and implementation of Data Lake and Data Warehouse solutions.

• 5+ years of experience in Hadoop, PySpark and Big data technologies.

• 5+ years relational database experience.

• Strong UNIX Shell scripting and Python experience to support data warehousing solutions.

• Power BI Development and Azure

• Relational databases DB2, Sybase, and Teradata.

• DDL and DML writing skills are essential as well as being able to write complex SQLs for data analysis.

• Create high quality and optimized stored procedures and queries.

• Experience data modeling and transformation of large-scale data sources using SQL, Hadoop, Spark, Hive, Snowflake, Databricks, Teradata or other Big Data technologies.

• Knowledge of version control systems, such as GIT/BitBucket

• Data warehousing concepts (Facts, Dimensions, star and snowflake design, etc.)

• Experience with scheduling tools (ex: Tivoli, Autosys)

• Database Performance and Tuning

• Unix and Python Scripting

Desired Skills:

• Ability to architect an ETL solution and data conversion strategy.

• Strong understanding of Data warehousing domain.