Engineering, Product Data Engineering, Data Engineer, Analyst/Associate, Singapore

About Data Engineering

Data plays a critical role in every facet of the Goldman Sachs business. The Data Engineering group is at the core of that offering, focusing on providing the platform, processes, and governance, for enabling the availability of clean, organized, and impactful data to scale, streamline, and empower our core businesses.

Within Data Engineering, we focus on offering a comprehensive data platform, Legend, which we have made available as an open-source product.  Legend includes a full data modeling environment, as well as the execution of data access and controls, and a vast set of value-add products which allow our business users to operate more efficiently.

Leveraging our own Legend offering, our engineers build efficient data solutions that source, curate, and distribute critical data to our businesses, including financial product, pricing, transaction, and client reference data. Our engineers collaborate closely with the business to design and curate business-specific data models, and to transform and distribute data for optimal storage and retrieval.

 

Who We Look For

Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment. Are you looking to apply your quantitative skills while deepening your understanding across a vast array of products? Product Data Quality is a high-value risk management team under Data Engineering Division, and is responsible for the governance programs that ensure critical securities modelling and pricing data is accurate, complete, and scalable. This data is a critical input and dependency to most trade and client support functions. Product Data Quality (PDQ) is comprised of four teams, based in Salt Lake City, Warsaw, Bengaluru, and Singapore.

How You Will Fulfill York Potential 

• Design & develop modern data management tools to curate our most important data sets, models and processes, while identifying areas for process automation and further efficiencies
• Contribute to an open-source technology - https://github.com/finos/legend
• Engage with data consumers and producers in order to design appropriate models to suit enable the business
• Proactively solve data quality issues from both tactical and strategic perspectives
• Proactively work with market data and external vendors (e.g., Bloomberg, Reuters, S&P, etc..) to ensure we have the highest quality data in our systems

 

Basic Qualifications 

• 1+ years of relevant work experience in a team-focused environment
• In-depth knowledge of relational and columnar SQL databases, including database design
• Proven experience applying domain driven design to build complex business applications
• Deep understanding of multidimensionality of data, data curation and data quality
• Comfort with Agile operating models (practical experience of Scrum / Kanban)
• General knowledge of business processes, data flows and the quantitative models that generate or consume data
• Excellent communications skills and the ability to work with subject matter experts to extract critical business concepts
• Independent thinker, willing to engage, challenge or learn
• Ability to stay commercially focused and to always push for quantifiable commercial impact
• Strong work ethic, a sense of ownership and urgency
• Strong analytical and problem solving skills
• Establish trusted partnerships with key contacts and users across business and engineering teams 
• Highly motivated

 

Preferred Qualifications

• Java / Python / AWS / React
• Financial Services industry experience
• Experience with Pure/Legend

We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment. Are you looking to apply your quantitative skills while deepening your understanding across a vast array of products? Product Data Quality is a high-value risk management team under Data Engineering Division, and is responsible for the governance programs that ensure critical securities modelling and pricing data is accurate, complete, and scalable. This data is a critical input and dependency to most trade and client support functions. Product Data Quality (PDQ) is comprised of four teams, based in Salt Lake City, Warsaw, Bengaluru, and Singapore.