Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Internal Audit - Birmingham - Analyst / Associate - Data Engineer

Goldman Sachs
West Midlands
4 months ago
Applications closed

Related Jobs

View all jobs

Audit Data Analytics Manager

Audit Data Analytics Manager

Audit Data Analytics Manager

Data Scientist

Assistant Vice President, Data Analytics Transformation Project Manager & Scrum Master 12 months FTC

Corporate Complaints Data Analyst

Role
Internal Audit – Data Engineer 
 

Internal Audit
What We Do Internal Audit’s mission is to independently assess the firm’s internal control structure, including the firm’s governance processes and controls, risk management, capital and anti-financial crime framework. In addition, it is also to raise awareness of control risk and monitor the implementation of management’s control measures.
 


In doing so, internal Audit:
• Communicates and reports on the effectiveness of the firm’s governance, risk management and controls that mitigate current and evolving risk
• Raise awareness of control risk
• Assesses the firm’s control culture and conduct risks; and
• Monitors management’s implementation of control measures
Goldman Sachs Internal Audit is organized into global teams comprising of business and technology auditors that cover all the firm’s businesses and functions - securities, investment banking, consumer and investment management, risk management, finance, cyber-security and technology risk, and engineering
 


Who We Look For
Goldman Sachs Internal Audit comprises individuals from diverse backgrounds including chartered accountants, developers, risk management professionals, cybersecurity professionals, and data scientists. We are organized into global teams comprising business and technology auditors to cover all the firm’s businesses and functions, including securities, investment banking, consumer and investment management, risk management, finance, cyber-security and technology risk, and engineering.
 


Data Analytics
In Internal Audit, we ensure that Goldman Sachs maintains effective controls by assessing the reliability of financial reports, monitoring the firm’s compliance with laws and regulations, and advising management on developing smart control solutions. Embed Data Analytics team leverages its programming and analytical capabilities to build innovative data driven solutions. The team works closely with auditors to understand their pain points and develop data-centric solutions to address the same
 


Your Impact
As part of the third line of defense, you will be involved in independently assessing the firm’s overall control environment and its effectiveness as it relates to current and emerging risks and communicating the results to local/ global management. In doing so, you will be supporting the provision of independent, objective and timely assurance around the firm’s internal control structure, thereby supporting the Audit Committee, Board of Directors and Risk Committee in fulfilling their oversight responsibilities.
We are looking for a strong data scientist, passionate about using data to challenge the norm, to join our Embed Data Analytics team. The candidate will work closely with the audit teams to build innovative and reusable analytical tools that will help make audit testing more efficient and provide meaningful insights into the firm’s control environment
 


Responsibilities
• Perform Database related activities – Data Modeling, Data Engineering, Data Governance and maintenance of Entitlements
• Obtain/Manage requirements that are tailored to each audit project and provide the results that can be used to provide insight to auditors in terms of sample selection, control gap identification, completeness of data sources, and data integrity (., Data Blessing)
• Build production ready analytical tools to automate repeatable and reusable processes within IA using reporting tools such as Tableau, Spotfire or Qlikview
• Execute elected data analysis activities. Such activities may be defined as procedural or programmatic tasks related to the analysis, extraction, transformation, and uploading of data (structured and unstructured) (., ETL processes).
• Perform Data analysis activities that may also be supplemented by summarized technical narratives describing the integrity of specific automated controls.
• Write data analysis code (. Python, Java, or Slang)
• Identify areas for process standardization and implement automation techniques in applications used for audit process and Data Analytics
• Execute on Embed DA - Data strategy developed by IA management within the context of audit responsibilities, such as risk assessment, audit planning, creation of reusable tools and providing innovative solutions to complex problems
• Partner with audit teams to help identify risks associated with businesses and facilitate strategic data sourcing and develop innovative solutions to increase efficiency and effectiveness of audit testing
• Build and manage relationships and communications with Audit team members
 


Basic Qualifications
• 3+ years of experience with a minimum of bachelor’s in computer science, Math, or Statistics
• Strong experience in RDBMS/ SQL
• Exposure to ETL Processes and Data Engineering
• Experience in implementing Data Quality measures and entitlement models
• Familiarity in programming languages such as Python
• Strong team player with excellent communication skills (written and oral). Ability to communicate what is relevant and important in a clear and concise manner and ability to handle multiple tasks
• Self-driven and motivated to take up initiatives to improve our processes
 


Preferred Qualifications
• Experience with data analytics tools and techniques
• Experience with analytical/ statistical programs such as SAS, SPSS, and R
• Experience with visualization tools (Tableau, Spotfire or QlikView) is a plus
• Creativity/Innovation, ., ability to create new ways to improve current processes and develop practical solutions that add value to department
 


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 /careers. 
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.