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Neema Raphael, Chief Data Officer and Head of Data Engineering, Goldman Sachs

Neema Raphael, Chief Data Officer and Head of Data Engineering, Goldman Sachs

Describe your career to date

I joined Goldman Sachs in 2003 as an analyst in the Technology Division and have played a role in the firm’s data maturity journey over the past two decades. Early on, I saw the importance of data not just at Goldman Sachs, but in financial services more broadly, and have since spent my entire career working on data at the firm.

I have led various engineering teams in Goldman Sachs’ Core Engineering division and was a member of the Core Strats team within the Securities Division that built SecDb (Securities Database), Goldman Sachs’ proprietary software system created to price trades and assess risk for trading positions.

Prior to my current role, I served as the head of Research and Development Engineering, where I was responsible for determining the firm’s strategy for emerging technology, including digital assets and artificial intelligence. Today, I am Goldman Sachs’ Chief Data Officer and Head of Data Engineering, where I am responsible for leading the firm’s data team with a focus on platform engineering, content curation, and governance.

How are you developing the data literacy of your organization, including the skills of your data teams and of your business stakeholders?

In my role, my primary focus is to ensure Goldman Sachs is fully leveraging the potential of data to power our businesses, deliver unparalleled client service, and advance data standards in financial services and beyond. At Goldman Sachs especially, data is foundational to everything we do, directly informing decisions our teams make about today’s complex financial market structures.

At a firm that depends on the quality, timeliness, and correctness of data, we have taken an engineering-led approach to our data strategy. We have developed a series of data platforms to streamline and simplify the data lifecycle and achieve greater control over the way the firm manages data, while also supporting innovation and growth.

I have spent my entire career working on data at Goldman Sachs, but one of the projects I am most proud of is our work developing and open sourcing Legend. Legend is our data management and governance platform that enables both technical and non-technical users to develop data-centric applications and derive data-driven insights. We built and curated Legend over the past ten years and saw internally how powerful the platform was when it comes to data quality, governance, and breaking down silos. We recognized Legend could help our clients with data strategy challenges and that it could benefit the industry at large with data operability and standardization. So, we took the opportunity to open source the whole platform. Through the development of platforms like Legend, we are driving data literacy across our firm for both our engineers and business teams.

What role do you play in building and delivering conventional AI solutions, including machine learning models? Are you involved in your organization’s adoptions of generative AI?

Information is the lifeblood of financial services. In order to innovate, improve client service and drive digital transformation, it is necessary to have a strong foundation built on data. With the advancements being made in artificial intelligence (AI) across all industries, we are at a pivotal time for digital transformation and data is more essential than ever to that strategy, laddering up to the broader firmwide approach to technology.

As a financial institution over 150 years’ old, Goldman Sachs has a unique and differentiated set of data, including market data, analytics, and research, informing how businesses operate and how teams serve clients. Our commitment to our data strategy over the years has been instrumental in our ability to innovate and leverage emerging technology. In the age of generative AI, our strong data foundation has been critical as we continue to experiment with and implement select use cases of the technology.

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organization or just the data function?

In leading the Data Engineering organization at Goldman Sachs, I remain focused on three core areas: platform engineering, content curation, and governance. Data is at the center of everything we do and by taking a platform-based approach, we have helped operationalize important firmwide functions, from client service to AI.

The crux of the data strategy we have implemented can be defined by our commitment to treating data as a first-class asset. In practice, what that means is the way we think of data is similar to the way engineering organizations have long thought about code. We encourage viewing data as an asset rather than an expense – like software – which ensures that the data team thinks about all the pieces that come along with an asset up front, including observability, abstraction, API, and connectivity.

With platforms like Legend, we have established a strong foundation built on data, enabling the firm to operate more efficiently, improve client service, and drive innovation. In today’s increasingly complex and data-driven world, organizations across many industries are recognizing the value that data has in driving business success.

In leading our data efforts at Goldman Sachs, I remain committed to driving a collaborative, effective data strategy that supports our engineering organization to drive commercial impact. This is achieved by developing scalable, innovative, and impactful solutions for the firm’s clients, while also leading the industry in applying modern technology and tools to enable operating efficiency across the firm.

Neema Raphael
has been included in:

100 Brands 2024 (USA)

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Neema Raphael, Chief Data Officer and Head of Data Engineering, Goldman Sachs

Join our membership network of over 250 global senior data and AI leaders.

The trusted global collaboration and intelligence platform for data leaders.

Join our membership network of over 250
global senior data and AI leaders.