Lead Data Engineer - QIS Technology
MORE ABOUT THIS JOB
CONSUMER AND INVESTMENT MANAGEMENT DIVISION (CIMD)
The Consumer and Investment Management Division includes Goldman Sachs Asset Management (GSAM), Private Wealth Management (PWM) and our Consumer business (Marcus by Goldman Sachs). We provide asset management, wealth management and banking expertise to consumers and institutions around the world. CIMD partners with various teams across the firm to help individuals and institutions navigate changing markets and take control of their financial lives.
Goldman Sachs Asset Management (GSAM) is one of the world's leading investment managers. GSAM provides institutional and individual investors with investment and advisory solutions, with strategies spanning asset classes, industries, and geographies. We help our clients navigate today's dynamic markets, and identify the opportunities that shape their portfolios and long-term investment goals. We extend these global capabilities to the world's leading pension plans, sovereign wealth funds, central banks, insurance companies, financial institutions, endowments, foundations, individuals and family offices.
The GSAM Quantitative Investment Strategy (QIS) team manages across a variety of mandates including institutional portfolios, mutual funds and hedge funds, using sophisticated quantitative models that have been developed in an innovative research environment. The group is one of the largest direct quantitative managers in the world, and is recognized as an industry leader in quantitative portfolio management techniques. The team manages exposures to global stock, bond, currency and commodity markets to generate alpha and advanced beta strategies for our Clients' portfolios. As one of the longest-running quantitative teams in the industry, QIS has developed a strong reputation for innovation, excellence and teamwork.
The QIS Engineering team works in a close-knit environment with Portfolio Managers and senior revenue generators. We design and develop the proprietary platforms that drive our QIS business, spanning alternative Data Acquisition, Quantitative Research, Model Generation, Portfolio Construction, Trading and more. Primary Responsibilities:
RESPONSIBILITIES AND QUALIFICATIONS REQUIREMENTS
- This role will focus on leading the effort to advance data strategies for portfolio management teams across QIS
- Manage any necessary development team
- Evolve state-of-the-art data infrastructure to drive the investment process and thus the firms bottom line
- Work closely with Senior and Executive leadership, to ensure QIS Infrastructure platform remains best in class
- Designing, developing, and maintaining a world-class, high-performing data platform to enhance research and portfolio management
- Developing rigorous and scalable data management/analysis tools to support the data-intensive quantitative investment process
ABOUT GOLDMAN SACHS
- 5+ years acting as a Tech Lead for a team focused on Data Engineering, Data Pipelines, etc.
- Hands-on expertise in Hadoop and Hadoop ecosystem such as Spark/Scala
- Data ingestion and management, Data APIs (multi-language support), and Big Data processing
- Working experience with Enterprise Java development, JSI web stack, various visualization toolkits.
- Knowledge of leading technology trends and best practices
- Proven to in ETL and data processing, know how to transform data to meet business goals.
- Meaningful experience in using AWS stack is a plus ideal, but not required
The Goldman Sachs Group, Inc. is a leading global investment banking, securities and investment management firm that provides a wide range of financial services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals. Founded in 1869, the firm is headquartered in New York and maintains offices in all major financial centers around the world.
© The Goldman Sachs Group, Inc., 2020. All rights reserved Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Vet.