Morgan Stanley Wealth Management Technology is looking for a Lead Business Intelligence & Data Analytics Engineer to develop and maintain innovative software solutions, partnering with Business and Technology teams to provide timely actionable analysis/insights to measure, grow, and supervise business areas, products, and services. The role involves crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques.
Requirements
- 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.
Responsibilities
- 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.
Other
- Ability to architect an ETL solution and data conversion strategy.
- Strong understanding of Data warehousing domain.
- Hadoop processing Performance Optimization.
- Familiarity with Python, Perl, R, data manipulation and analysis.
- Experience in Agile methodology.
