Who We Look For: 

Goldman Sachs Engineers are innovators and problem-solvers, building solutions for various divisions. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.

We are seeking a high-caliber, hands-on Data Engineering Manager to lead our Wealth Data Engineering team in Birmingham. In this pivotal role, you will be responsible for the architectural vision and operational excellence of our data platforms. You will not only manage a team of talented engineers but also "raise the bar" by contributing to code, designing scalable cloud architectures, and ensuring our data ecosystem is fully optimized for Advanced Analytics and Artificial Intelligence (AI).

As the local lead, you will bridge the gap between complex financial data requirements and cutting-edge engineering solutions, ensuring our wealth management services are powered by reliable, real-time, and AI-ready data.

Key Responsibilities: 

• Team Leadership: Lead and mentor a local team of data engineers in Birmingham. Foster a culture of technical excellence, continuous learning, and agile delivery.
• Platform Ownership: Own the end-to-end data lifecycle. Design, build, and maintain robust data platforms and pipelines that handle large-scale, multi-structured wealth data.
• AI Readiness: Implement strategies to make data "AI-ready," focusing on data quality, low-latency access, and the integration of MLOps principles into the data engineering workflow.
• Technical Governance: Set and enforce high standards for code quality, documentation, and testing. Drive the adoption of modern engineering practices like CI/CD, Infrastructure as Code (Terraform), and DataOps.
• Stakeholder Management: Collaborate with Data Scientists, Product Managers, and Application teams to translate business needs into scalable technical solutions.
• Local Advocacy: Act as the technical face of the team in the Birmingham office, contributing to the local tech community and internal engineering forums.

Qualifications: 

Technical Requirements

• Experience: 10+ years in Data Engineering, with at least 2+ years in a formal leadership or management role.
• Data Platform Mastery: Proven experience with modern data platforms such as Snowflake (AI Data Cloud) and cloud-native services. Good understanding of open-source table formats, specifically Apache Iceberg, to enable interoperability, schema evolution, and high-performance analytics across multiple engines.
• Programming: Expert-level proficiency in  Java, Python and SQL. 
• Big Data & Orchestration: Hands-on experience with Spark, Kafka, and orchestration tools like Apache Airflow, Dagster, or dbt.
• Data Modeling: Deep understanding of data warehousing and modern data lakehouse architecture.

Leadership & Soft Skills

• Mentorship: Proven track record of upskilling junior and senior engineers.
• Communication: Ability to explain complex technical concepts to non-technical stakeholders in the wealth management business.
• Problem Solving: A "builder" mindset with the ability to navigate ambiguity in a fast-paced environment.

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.