Data engineer at Barclays (posted via HireHi) in the London office, building and maintaining ETL pipelines, data warehouses, and data lakes for banking and post-trade data, and collaborating with data scientists on ML models. Core stack includes SQL, ETL/data transformation, and distributed data environments, with AWS and data lake exposure nice to have.
Barclays is a banking and financial services company that provides markets and post-trade technology, including systems for collecting, storing, processing, and analysing data.
Задачи:
Build and maintain data architecture pipelines for transferring and processing durable, complete, and consistent data
Design and implement data warehouses and data lakes that manage required data volumes and velocity while meeting security measures
Develop processing and analysis algorithms for the intended data complexity and volumes
Collaborate with data scientists to build and deploy machine learning models
Advise and influence decision-making and contribute to policy development
Lead collaborative assignments and guide team members through structured assignments
Identify new directions for assignments and projects and combine cross-functional methodologies to meet required outcomes
Consult on complex issues and advise People Leaders on escalated issues
Identify ways to mitigate risk and develop policies and procedures supporting control and governance
Own risk management and strengthen controls related to the work
Collaborate with other work areas and business-aligned support functions
Analyse data from multiple internal and external sources to solve problems creatively and effectively
Communicate complex information to stakeholders
Influence stakeholders to achieve outcomes
Требования:
Strong hands-on experience in ETL development, data transformation, and end-to-end pipeline workflows
Experience creating structured datasets and analytics outputs
Strong SQL, database querying, joins, and large-scale data modelling skills
Experience with data warehousing and dataset integration
Experience integrating multiple enterprise data sources, including APIs and feeds
Experience working with distributed data environments
Ability to debug pipelines, resolve data issues, and validate datasets
Experience with data quality frameworks and controls
Understanding of financial datasets
Ability to collaborate across teams and stakeholders
Ability to advise and influence decision-making
Ability to lead collaborative assignments and guide team members
Ability to analyse complex data from multiple sources and communicate complex information
Nice to have: Exposure to AWS, data lake architectures, catalogue-based querying environments, and machine learning model delivery