Internal Audit, Data Strategy & Analytics, Executive Director, Singapore

ABOUT OUR ROLES

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

InternalAudit’s mission isto independently assess the firm’s internal control structure, includinggovernance processes and controls, risk management, capital and anti-financialcrime frameworks. Internal Audit communicates on the effectiveness ofgovernance, risk management and controls, raises awareness of control risk,assesses control culture and conduct risk, and monitors management’simplementation of control measures.

DataStrategy and Analytics (DSA) partners with audit teams to translate dataneeds 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.

JOB RESPONSIBILITIES

As an Executive Director 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.

• 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.

PREFERREDQUALIFICATIONS

• 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.

WHATSUCCESSS LOOKS LIKE

• Data solutions arescalable, controlled, well-documented and reusable across audit teams.
• Engineering decisionsare transparent, risk-aware and supported by strong design and reviewpractices.
• The team developsstronger technical capability, while stakeholders receive clear, dependabledelivery.

ABOUTGOLDMAN SACHS

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

We're committed tofostering and advancing diversity and inclusion in our own workplace and beyondby ensuring every individual within our firm has a number of opportunities togrow professionally and personally, from our training and development opportunitiesand firmwide networks to benefits, wellness and personal finance offerings andmindfulness programs. Learn more about our culture, benefits, and people atGS.com/careers.

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

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