Internal Audit - Data Engineering - Associate - Birmingham
Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Oct 14, 2026. Goldman Sachs scores A on the Alion truth index.
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 an Associate in DSA, you will design and implement production-grade pipelines, models and data products for the third line of defence. You will work closely with audit teams and data owners to source, transform and curate data, turning complex requirements into reliable solutions that can be reused across audit coverage.
RESPONSIBILITIES
- Design, build, test and support production-grade ETL/ELT pipelines that source, transform and curate data for Internal Audit.
- Develop maintainable Python solutions using Pandas, Polars and PySpark, selecting fit-for-purpose approaches based on scale, performance and operational needs.
- Write advanced SQL for data transformation, reconciliation, profiling and analysis across OLTP and analytical data platforms.
- Design data models for operational and analytical use cases, including dimensional modelling, slowly changing dimensions, lakehouse and data-lake patterns.
- Work with platforms such as Snowflake and relational databases including DB2 and PostgreSQL, applying sound principles for schema design, performance and data movement.
- Implement data-quality checks, controls, lineage, observability, documentation and automated tests to improve trust and supportability.
- Contribute to AI-ready data products and semantic layers that provide consistent business meaning and governed access to data.
- Partner with audit teams and data owners to understand requirements, resolve data discrepancies and deliver reusable solutions.
- Participate in design reviews, code reviews, version-control and CI/CD practices, and provide effective production support.
- Communicate progress, issues and technical decisions clearly to both technical and non-technical stakeholders.
BASIC QUALIFICATIONS
- 3+ years of relevant data engineering, software engineering or data-platform experience.
- Bachelor’s degree or equivalent practical experience in computer science, engineering or a related quantitative discipline. A master’s degree is welcome.
- Hands-on proficiency in Python, including Pandas, Polars and PySpark, with experience writing testable and maintainable code.
- Strong SQL proficiency and practical understanding of OLTP databases, relational design and query performance.
- Strong understanding of data modelling, data warehousing, dimensional modelling and slowly changing dimensions.
- Experience designing and implementing ETL/ELT pipelines and working with data-lake, lakehouse or cloud data
- Strong data analytics and problem-solving skills, including the ability to profile, interpret and analyse data
- Clear written and verbal communication and the ability to collaborate across technical and non-technical teams.
PREFERRED QUALIFICATIONS
- Experience with Snowflake and data integration from DB2, PostgreSQL or comparable enterprise platforms.
- Exposure to Spark-based processing, orchestration frameworks, CI/CD, containers and cloud data services.
- Familiarity with semantic layers, metadata management and development of governed, AI-ready data products.
- Understanding of data governance, privacy, security, lineage and retention expectations.
- Experience in financial services, audit, risk or controls is helpful but not required.
WHAT SUCCESS LOOKS LIKE
Pipelines and data products are reliable, testable, supportable and delivered with clear documentation. Data issues are identified early, investigated rigorously and resolved through sustainable engineering solutions. Audit stakeholders can use consistent, trusted data more efficiently.
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: https://www.goldmansachs.com/careers/footer/disability-statement.html
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
© The Goldman Sachs Group, Inc., 2026. All rights reserved.
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.