About The Role

Internal Audit | Data Strategy and Analytics | Hyderabad

Lead the architecture and delivery of scalable data capabilities that strengthen how Internal Audit sources, governs and uses data, while remaining hands-on in engineering and developing the team.

WHAT WE DO

Internal Audit’s mission is to independently assess the firm’s internal control structure, including governance processes and controls, risk management, capital and anti-financial crime frameworks. Internal Audit communicates on the effectiveness of governance, risk management and controls, raises awareness of control risk, assesses control culture and conduct risk, and monitors management’s implementation of control measures.

Data Strategy and Analytics (DSA) partners with audit teams to translate data needs into durable, reusable capabilities. The team designs data products, engineering patterns and AI-ready data foundations that improve the efficiency, consistency and insight of audit work.

YOUR IMPACT

As a Vice President in DSA, you will combine architecture, hands-on engineering, delivery leadership and mentoring. You will shape scalable data solutions for the third line of defence, set engineering standards and guide teams from problem definition through production delivery. You will work with auditors, engineers and data owners to ensure solutions are reliable, controlled and aligned to Internal Audit priorities.

RESPONSIBILITIES

Own solution architecture and technical delivery for scalable data platforms, pipelines and curated data products supporting Internal Audit use cases.
Translate audit and control objectives into pragmatic data designs, balancing delivery speed, reusability, performance, security and maintainability.
Design and review data models for operational and analytical workloads, including dimensional models, slowly changing dimensions, lakehouse and data-lake patterns.
Remain hands-on with Python and SQL, providing technical direction for implementations using Pandas, Polars and PySpark as appropriate to scale and workload
Establish engineering standards for ETL/ELT design, testing, observability, data quality, lineage, documentation, version control and controlled production deployment.
Guide the use of Snowflake and relational platforms such as DB2 and PostgreSQL, with clear expectations for workload design, performance and integration.
Lead the design of AI-ready data products and semantic layers that make governed data easier to discover, interpret and use in analytical and AI-enabled solutions.
Drive delivery across multiple stakeholders, communicate technical trade-offs clearly, manage dependencies and escalate material risks promptly.
Mentor engineers, strengthen design and code-review practices, and contribute to hiring, capability development and an inclusive engineering culture.
Build productive relationships with audit teams, technology partners, data owners and governance functions across the firm.

BASIC QUALIFICATIONS

7+ years of relevant data engineering or software engineering experience, including technical leadership of complex data solutions.
Bachelor’s degree or equivalent practical experience in computer science, engineering or a related quantitative discipline. A master’s degree is welcome.
Advanced, hands-on proficiency in Python, including Pandas, Polars and PySpark, and strong software-engineering practices.
Advanced SQL proficiency and strong understanding of OLTP databases, relational design and query performance.
Demonstrated expertise in data modelling, data warehousing, dimensional modelling and slowly changing dimensions.
Experience designing ETL/ELT solutions and scalable data architectures across data lake, lakehouse or cloud data warehouse patterns.
Ability to lead architecture and delivery while explaining technical choices, risks and trade-offs to senior stakeholders.
Demonstrated ability to mentor engineers, raise technical standards and coordinate delivery across teams.
Strong written and verbal communication, sound judgement and the ability to manage multiple priorities

PREFERRED QUALIFICATIONS

Experience architecting solutions on Snowflake and integrating data from DB2, PostgreSQL or comparable enterprise platforms.
Experience with Spark-based processing, orchestration frameworks, CI/CD, containers and cloud-native delivery patterns.
Experience designing governed semantic layers and AI-ready data products, including metadata, access controls and reusable definitions.
Familiarity with data governance, privacy, security, retention, lineage and model-risk considerations relevant to enterprise data. Experience in financial services, audit, risk or controls is helpful but not required.

WHAT SUCCESS LOOKS LIKE

Data solutions are scalable, controlled, well-documented and reusable across audit teams.
Engineering decisions are transparent, risk-aware and supported by strong design and review practices.
The team develops stronger technical capability, while stakeholders receive clear, dependable delivery.

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 with offices around the world. We believe who you are makes you better at what you do. We are committed to fostering and advancing diversity and inclusion in our workplace and to providing opportunities for professional and personal growth. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more about reasonable accommodations

Position-description note: This document describes the duties most frequently performed at this position level and is not intended to be a complete list of assigned duties. The role is performed within a professional office environment. Applicable health and safety policies are available to workers upon request