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Compute Infrastructure Platforms Lead Data Engineer

JPMorganChase
2 days ago
Full-time
On-site
London, United Kingdom
Data Engineer
Description

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

As a Lead Data Engineer at JPMorganChase within the [insert LOB or sub LOB], you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

 

  • Ensures consumers are able to have a high level of trust in the analysis they produce based on the data our product line emits.
  • Generates data models for their team using firmwide tooling, linear algebra, statistics, and geometrical algorithms
  • Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
  • Implements database back-up, recovery, and archiving strategy 
  • Evaluates and reports on access control processes to determine effectiveness of data asset security​ with minimal supervision
  • Adds to team culture of diversity, opportunity, inclusion, and respect

 

Required qualifications, capabilities, and skills

 

  • Working knowledge of Infrastructure Platforms and Enterprise Server Operating Systems
  • Working experience with both relational and NoSQL databases​
  • Experience and proficiency across the data lifecycle
  • Experience with database back-up, recovery, and archiving strategy
Preferred qualifications, capabilities, and skills
 
  • Proven experience working in SRE or Infrastructure Deployment roles,
  • Mid level data pipeline or analytical product creation
  • Proven Data Quality issue root cause analysis