Engineering-L2-Bengaluru-Vice President-Product Management

Bengaluru, Karnataka, India

About the role

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 our Technology Division and global strategists groups, 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.

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.

You will act as a force multiplier for Data Platform leadership — translating strategy into a sequenced plan, making the state of the world visible, unblocking engineering teams, and personally executing on the initiatives that matter most. You will break complex, ambiguous problems into steps that teams can act on, adopting the principles of early releases and incremental delivery to generate evidence of value.

As a Technical Program Manager in Data Engineering, you will:

Contribute to one of the most critical Engineering functions at Goldman Sachs.
Own the roadmap — work with engineering leads to define milestones, sequence dependencies, surface trade-offs, and keep the plan honest as priorities shift.
Run the commitment book. Maintain a single, trusted view of every major commitment the organization has made — to divisions, to Engineering leadership, to Risk, to Audit, and to regulators — and drive them to closure, escalating early when they are at risk.
Own the risk book of work. Track and drive remediation of technology risk, control, audit, and regulatory items across the estate; partner with Risk, Compliance, and Engineering leads to ensure obligations are met on time.
Lead critical cross-cutting programs end to end. Platform migrations, adoption and decommissioning efforts, data model and API rollouts, query engine and performance initiatives, and AI-enablement programs — from framing through delivery and measurable outcome.
Steward the financials. Support leadership on budget planning and forecasting, headcount and vendor spend, and cloud consumption and cost efficiency for the Lakehouse estate; build the reporting that lets leaders make cost decisions with evidence.
Support the people agenda. Partner with leadership on organizational planning, hiring pipelines, onboarding, location strategy, engagement, and talent and performance processes.
Go deep technically. Become a proficient user of the Legend platform; understand our data models, data APIs, connection patterns, and query workloads well enough to challenge assumptions, spot dependencies others miss, and represent the platform credibly to senior stakeholders.
Use data to run the business. Build and maintain the metrics that describe platform health, adoption, delivery throughput, cost, and user experience.
Partner with AI initiatives. Help ensure new AI-driven capabilities are delivered in line with the firm's data governance and AI standards.
Establish robust relationships with our divisional partners and users so that what we build lands with impact, and connect the work of the team to the firm's commercial outcomes.
Improve how the organization runs — sharpen planning, reporting, and governance rhythms, and shape standards within the firmwide Engineering community.

BASIC QUALIFICATIONS

Bachelor’s degree in computer science, or a related technical and/or business discipline, or equivalent practical experience
8+ years of technical program or project management experience delivering complex engineering programs, ideally within a data, platform, or infrastructure organization
Working knowledge of data and cloud fundamentals: strong SQL, relational and analytical databases, data modelling concepts, APIs, and public cloud.
Demonstrated success partnering with engineering teams to deliver technical outcomes — not just tracking them
Track record of managing dependencies, risks, and issues across multiple teams, and of driving items to closure without direct authority
Comfortable with Agile ways of working and with the planning and reporting mechanics that support them
Demonstrated ability to communicate complex technical problems, plans, and trade-offs clearly to both engineers and senior non-technical stakeholders
Ability to set goals, meet deadlines and effectively communicate progress to stakeholders
Exceptional organization and attention to detail — you are the person who holds the thread when nobody else does
Strong analytical and problem-solving skills
Independent thinker, willing to engage, challenge or learn
Ability to stay commercially focused and to always push for quantifiable commercial impact
A healthy obsession with the quality of the platform and the experience of the engineers and users who depend on it, and a willingness to be their internal advocate
Proven ability to lead by example, with a positive, solutions-oriented attitude
Strong work ethic, a sense of ownership and urgency

PREFERRED QUALIFICATIONS

Master's degree or MBA in Computer Science, Engineering, or a related field
Hands-on exposure to modern Lakehouse and data platform technologies — for example object storage, open table formats (Apache Iceberg, Delta Lake), Spark, Snowflake, Databricks, BigQuery, or comparable
Familiarity with data modelling and semantic modelling approaches, data APIs, data contracts, and data governance, lineage, and access control concepts
Understanding of AI and machine learning delivery, including the model development lifecycle, retrieval-augmented generation and LLM-based applications, and the data foundations required to support them
Scripting or data manipulation skills in Python
Experience supporting a technology risk, controls, audit, or regulatory book of work
Prior experience in financial services or another highly regulated, data-intensive environment

ABOUT GOLDMAN SACHS

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.