Associate/Vice President - Sustainable Finance Data Engineer – Geospatial
Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm's employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices in 43 countries. As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, to achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.
The Global Sustainable Finance Group ("GSF") aims to drive the growth of sustainable investing through ongoing development of products and solutions, economic analysis, thought leadership and capacity building initiatives.
The GSF team is seeking a Data Engineer to support the team's activities, with a particular focus on the team's expanding efforts in geospatial data and analytics.
Successful candidates will have a dual passion for sustainability and technology, as well as demonstrated expertise in data engineering and distributed systems.
Additionally, successful candidates will be well-organized and detail oriented, and will work well in a collaborative team environment.
The Data Engineer will be responsible for:
-Working with GSF data team and various internal partners to maintain the existing infrastructure for ingesting, fusing, and utilizing data.
-Extending the existing data infrastructure to accommodate geospatial datasets and their derivatives.
-Collaborating with GSF economists and data scientists to design and implement geospatial analytics. Qualifications:
-Bachelors or Masters in Computer Science, Software Engineering, Geography, or a similar field, with strong exposure to data infrastructures underpinning geospatial and financial time-series data analytics.
-Experience with data engineering, building, deploying and maintaining large, complex data services and pipelines on distributed systems.
-Fluency in Python, SQL, Linux, and experience with workflow scheduling tools.
-Experience implementing large-scale geo-processing and geo-statistical tasks.
-Experience maintaining and curating meta-data libraries and Git code repositories, and supporting notebook-based data science workflow.
Exposure to one or more of the following areas is a plus:
-Efficiently integrating multi-source (e.g. remote sensed, financial, and sentiment data) and multi-frequency (ranging from annual to sub-second) datasets in a common analytical framework.
-Experience implementing machine learning algorithms of prediction and classification.
-Experience with integrating data pipelines with front-end applications and business intelligence tools.
-Experience with bulk financial markets data. #LI-GH1