Internal Audit - Data Engineering - Associate - Birmingham
Goldman Sachs29 days ago
Birmingham, United KingdomMid Level
Responsibilities
- Design, build, test, and support production-grade ETL/ELT pipelines for Internal Audit data.
- Develop maintainable Python solutions using Pandas, Polars, and PySpark.
- Write advanced SQL for transformation, reconciliation, profiling, and analysis across operational and analytical platforms.
- Design operational and analytical data models using dimensional modeling, slowly changing dimensions, lakehouse, and data-lake patterns.
- Work with Snowflake, DB2, PostgreSQL, and other relational or enterprise data platforms.
- Implement data-quality checks, controls, lineage, observability, documentation, and automated tests.
- Contribute to semantic layers and governed, AI-ready data products.
- Partner with audit teams and data owners to gather requirements, resolve discrepancies, and deliver reusable solutions.
- Participate in design reviews, code reviews, version control, CI/CD practices, and production support.
- Communicate technical decisions, progress, and issues to technical and non-technical stakeholders.
Requirements
- At least 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.
- Hands-on Python proficiency, 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 modeling, data warehousing, dimensional modeling, and slowly changing dimensions.
- Experience designing and implementing ETL/ELT pipelines and working with data-lake, lakehouse, or cloud data platforms.
- Strong data analytics and problem-solving skills, including data profiling, interpretation, and analysis.
- Clear written and verbal communication and ability to collaborate across technical and non-technical teams.
- Preferred experience with Snowflake and data integration from DB2, PostgreSQL, or comparable enterprise platforms.
- Preferred exposure to Spark-based processing, orchestration frameworks, CI/CD, containers, and cloud data services.
- Preferred familiarity with semantic layers, metadata management, governed AI-ready data products, and data governance, privacy, security, lineage, and retention expectations.
- Experience in financial services, audit, risk, or controls is helpful but not required.
Tech Stack
Categories
Data Engineering
About Goldman Sachs
Goldman Sachs is a global financial services firm that provides investment banking, securities trading, and asset and wealth management to corporations, financial institutions, governments, and individuals. Its businesses generate revenue from advisory and underwriting fees, trading and financing, and management fees on client assets. Founded in 1869 and headquartered in New York City, it operates worldwide and is listed on the NYSE under the ticker GS.