Описание
Goldman Sachs is a global investment banking, securities and investment management firm. The Lakehouse and AI Data Platform team builds data foundations that support the firm’s AI and analytics capabilities.
Задачи
- Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform
- Refactor and modernise existing data flows to improve reliability, performance and maintainability
- Build reusable tooling to improve delivery, consistency and operational support
- Ensure data pipelines are production-ready, well tested and operationally supportable
- Develop raw, refined and curated datasets for analytics, reporting and AI use cases
- Apply data modelling principles to represent business entities, relationships and historical change
- Work with consumers to shape usable, documented data products aligned with business needs
- Implement controls to validate data completeness, accuracy and consistency
- Use reconciliation approaches to validate production outputs and investigate data breaks
- Contribute to standards for testing, monitoring and issue resolution
- Improve testing, monitoring and reconciliation tooling to strengthen platform reliability and delivery
- Work with engineers, platform teams and data consumers to deliver agreed outcomes on time and to quality expectations
- Communicate progress, risks, dependencies and design choices
- For more senior candidates, contribute to technical leadership, task breakdown and support for junior engineers
Требования
- Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience
- Strong quantitative skills or data engineering expertise
- Strong hands‑on programming experience in Python or Java
- Good working knowledge of SQL, including troubleshooting, optimisation and data analysis
- Ability to learn new tools, internal platforms and delivery workflows quickly
- Familiarity with version control, testing, release discipline and CI/CD practices
- Understanding of temporal data modelling, schema design, schema evolution and data compatibility
- Understanding of partitioning, clustering and other techniques for improving data performance at scale
- Ability to choose between normalised and denormalised models and between natural and surrogate keys
- Practical approach to data quality, reconciliation and root‑cause analysis
- Experience building or supporting production data pipelines in a collaborative engineering environment
- Experience with distributed data processing frameworks such as Apache Spark
- Working knowledge of JSON, Avro and Parquet
- Nice to have: technical design ownership across multiple datasets or pipeline domains, experience guiding implementation standards and engineering practices, ability to lead delivery for a workstream and support less experienced engineers
Условия
- The role is based in London, England, United Kingdom
- Training and development opportunities, firmwide networks, benefits, wellness and personal finance offerings, and mindfulness programs are available
- Reasonable accommodations are available for candidates with special needs or disabilities during the recruiting process
#J-18808-Ljbffr
platform engineer for AI data platforms employer: Enfint
As a leading innovator in AI products for major publishers, our company offers an inspiring work environment where creativity and technology intersect. We prioritise employee growth through continuous learning opportunities and foster a collaborative culture that values diverse perspectives. Located in a vibrant city, we provide competitive salaries, relocation support, and the chance to make a real impact in the media landscape.
