platform engineer for AI data platforms

ОписаниеGoldman 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.ЗадачиBuild, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platformRefactor and modernise existing data flows to improve reliability, performance and maintainabilityBuild reusable tooling to improve delivery, consistency and operational supportEnsure data pipelines are production-ready, well tested and operationally supportableDevelop raw, refined and curated datasets for analytics, reporting and AI use casesApply data modelling principles to represent business entities, relationships and historical changeWork with consumers to shape usable, documented data products aligned with business needsImplement controls to validate data completeness, accuracy and consistencyUse reconciliation approaches to validate production outputs and investigate data breaksContribute to standards for testing, monitoring and issue resolutionImprove testing, monitoring and reconciliation tooling to strengthen platform reliability and deliveryWork with engineers, platform teams and data consumers to deliver agreed outcomes on time and to quality expectationsCommunicate progress, risks, dependencies and design choicesFor more senior candidates, contribute to technical leadership, task breakdown and support for junior engineersТребованияBachelor’s or master’s degree in a relevant discipline, or equivalent practical experienceStrong quantitative skills or data engineering expertiseStrong hands‑on programming experience in Python or JavaGood working knowledge of SQL, including troubleshooting, optimisation and data analysisAbility to learn new tools, internal platforms and delivery workflows quicklyFamiliarity with version control, testing, release discipline and CI/CD practicesUnderstanding of temporal data modelling, schema design, schema evolution and data compatibilityUnderstanding of partitioning, clustering and other techniques for improving data performance at scaleAbility to choose between normalised and denormalised models and between natural and surrogate keysPractical approach to data quality, reconciliation and root‑cause analysisExperience building or supporting production data pipelines in a collaborative engineering environmentExperience with distributed data processing frameworks such as Apache SparkWorking knowledge of JSON, Avro and ParquetNice 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 engineersУсловияThe role is based in London, England, United KingdomTraining and development opportunities, firmwide networks, benefits, wellness and personal finance offerings, and mindfulness programs are availableReasonable accommodations are available for candidates with special needs or disabilities during the recruiting process

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