You will be taken to Barclays’s own careers page to apply. Job ID JR-0000128770.
Job description
Job Description
Purpose of the role To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure.
Accountabilities
Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
Development of processing and analysis algorithms fit for the intended data complexity and volumes.
Collaboration with data scientist to build and deploy machine learning models.
Analyst Expectations
To perform prescribed activities in a timely manner and to a high standard consistently driving continuous improvement.
Requires in-depth technical knowledge and experience in their assigned area of expertise
Thorough understanding of the underlying principles and concepts within the area of expertise
They lead and supervise a team, guiding and supporting professional development, allocating work requirements and coordinating team resources.
If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
OR for an individual contributor, they develop technical expertise in work area, acting as an advisor where appropriate.
Will have an impact on the work of related teams within the area.
Partner with other functions and business areas.
Takes responsibility for end results of a team’s operational processing and activities.
Escalate breaches of policies / procedure appropriately.
Take responsibility for embedding new policies/ procedures adopted due to risk mitigation.
Advise and influence decision making within own area of expertise.
Take ownership for managing risk and strengthening controls in relation to the work you own or contribute to. Deliver your work and areas of responsibility in line with relevant rules, regulation and codes of conduct.
Maintain and continually build an understanding of how own sub-function integrates with function, alongside knowledge of the organisations products, services and processes within the function.
Demonstrate understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
Make evaluative judgements based on the analysis of factual information, paying attention to detail.
Resolve problems by identifying and selecting solutions through the application of acquired technical experience and will be guided by precedents.
Guide and persuade team members and communicate complex / sensitive information.
Act as contact point for stakeholders outside of the immediate function, while building a network of contacts outside team and external to the organisation.
All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.
Step into the role of Cloud Data Engineer at Barclays, where you'll help shape and deliver our cloud-first data strategy. This role will design and build scalable AWS data platforms, analytics capabilities, and reusable data products. You will work in a collaborative engineering team to move trusted data from golden-source systems into governed cloud data layers and make it available for analytics and downstream consumption.
You may be assessed on key critical skills relevant for success in role such as:
Contribute to the cloud data strategy and translate business and analytical requirements into secure, scalable AWS data solutions.
Design, build, test, deploy, and support batch and event-driven data pipelines using AWS services such as AWS Glue, Step Functions, Lambda, Athena, S3, and EC2.
Use enterprise hydration frameworks to ingest data from golden-source systems into governed source-aligned layers in the cloud.
Develop curated data models and reusable data products that are discoverable, trusted, and suitable for analytics, reporting, and downstream applications.
Design and implement data lake and data warehouse patterns, including appropriate partitioning, file formats, metadata, and performance optimization.
Develop robust transformation logic using SQL and Python or PySpark, with appropriate testing, reconciliation, and error handling.
Implement workflow orchestration, scheduling, monitoring, alerting, restart, and recovery capabilities for production data pipelines.
Apply data quality, lineage, metadata, security, access-control, retention, and audit requirements throughout the data lifecycle.
Optimize cloud data workloads for performance, reliability, scalability, and cost.
Collaborate with data architects, platform engineers, source-system teams, analysts, product owners, and governance stakeholders.
Follow engineering standards for source control, peer review, automated testing, CI/CD, documentation, and operational support.
Investigate production issues, perform root-cause analysis, and implement sustainable fixes and preventative controls.
Essential Skills / Qualifications
Relevant data engineering experience, including the design and delivery of production-grade data pipelines and analytical data platforms.
Strong SQL skills and a solid understanding of data warehousing, dimensional modelling, data lake concepts, ETL/ELT patterns, and data lifecycle management.
Hands-on experience building cloud data solutions on AWS using AWS Glue, Step Functions, Lambda, Athena, S3, and EC2.
Proficiency in Python and/or PySpark for data processing, automation, and integration.
Experience ingesting data from relational databases, files, APIs, and other enterprise sources into governed cloud data layers.
Experience working with reusable ingestion or hydration frameworks and configurable, metadata-driven pipeline patterns.
Ability to build curated data products from source-aligned data, including transformation, validation, reconciliation, and publication for consumers.
Knowledge of data quality, metadata, lineage, security, encryption, identity and access management, monitoring, and audit controls.
Experience with Git-based source control, automated testing, CI/CD, and deployment across development, test, and production environments.
Strong analytical, troubleshooting, documentation, stakeholder-management, and written and verbal communication skills.
Desirable / Good-to-Have Skills
Experience with Databricks, including Apache Spark, Delta Lake, notebooks, workflows, and Unity Catalog.
Experience with Astronomer or Apache Airflow for workflow orchestration and operational monitoring.
Knowledge of AWS services such as Redshift, EMR, Lake Formation, Kinesis, DynamoDB, CloudWatch, IAM, and CloudFormation.
Experience with infrastructure-as-code tools such as Terraform or CloudFormation.
Familiarity with data mesh or data-product principles, including ownership, discoverability, interoperability, and service-level expectations.
Experience with data observability, schema evolution, data contracts, and cloud cost optimization.
Relevant AWS or Databricks certification.
Experience in banking or financial services and working within enterprise risk, security, and compliance frameworks.
You may be assessed on key essential skills relevant for success in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job-specific technical skills.
