Internal Audit - Data Engineering - Associate

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.

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.

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.

• 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.

• 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.

• 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,…