What We Do
At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.
Engineering, which is comprised of core and business-aligned teams, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.
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.
As a Full-stack Software Engineer on the Data Engineering team, you will be responsible for helping improve the Legend data platform, our curated data offerings, and how the business uses data. We tackle some of the most complex engineering problems across distributed software development, optimizing data access and delivery, enabling core access controls via well-defined security paradigms, building UIs to enable data visualization, using machine learning to curate data, or engaging with businesses to ensure their data needs are met, and we react quickly to new demands by rapidly evolving our data solutions.
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
- Drive adoption of cloud technology for data processing and warehousing
- Engage with data consumers and producers in order to design appropriate models to suit enable the business
Relevant Technologies: Java, Python, AWS, React
Basic Qualifications
- A Bachelor or Master degree in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline)
- 2-7+ years of relevant work experience in a team-focused environment
- 2-7+ years of experience in distributed system design
- 2-7+ years of experience using Java, Python, and/or React
- 2-7+ years of experience or interest in functional programming languages
- Strong object-oriented design and programming skills and experience in OO languages (Java)
- Strong experience with cloud infrastructure (AWS, Azure, or GCP) and infrastructure as code (Terraform, CloudFormation, or ARM templates).
- Proven experience applying domain driven design to build complex business applications
- Deep understanding of multidimensionality of data, data curation and data quality, such as traceability, security, performance latency and correctness across supply and demand processes
- In-depth knowledge of relational and columnar SQL databases, including database design
- Expertise in data warehousing concepts (e.g. star schema, entitlement implementations, SQL v/s NoSQL modeling, milestoning, indexing, partitioning)
- Experience in REST and/or GraphQL
- Experience in creating Spark jobs for data transformation and aggregation
- 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
Preferred Qualifications
- Financial Services industry experience
- Experience with Pure/Legend
- Working knowledge of open-source tools such as AWS lambda, Prometheus
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