Build and support GCP-based data pipelines within a retail banking group's Mobile Frameworks Lab. The work improves how customers interact with the bank, using BigQuery, Dataflow, Composer/Airflow, Cloud Run and dbt.
Hybrid role in London or Bristol on a one-year contract. You will keep production data pipelines running through monitoring, troubleshooting, incidents, change requests and problem management. AI, machine learning and monitoring tools such as Dynatrace and Splunk are desirable.
The Role
We’re on an exciting journey to transform our Group and the way we’re shaping finance for good. We’re focusing on the future, investing in our technologies, workplaces, and colleagues to make our Group a great place for everyone. Including you!As the UK's largest Digital, Retail and Commercial bank we realise that our data is a significant business differentiator and plays a pivotal role in achieving our sustainable growth ambitions.This role in is in the Mobile Frameworks Lab, where we’re on a mission to develop innovative solutions for understanding and improving how our customers interact with the bank.
Your responsibilities
Essential skills/knowledge/experience
Strong hands-on experience with GCP ecosystem (BigQuery, Dataflow, Composer/Airflow, Cloud Storage, Cloud Run)
Proficiency in SQL, Python / PySpark for data engineering and automation
Experience in supporting data pipelines, including monitoring and troubleshooting
Hands-on experience with DBT (Data Build Tool) for data transformation and modelling, enabling modular, reusable, and testable data pipelines
Experience with our client schedulers (e.g., TWSD) and cloud-native orchestration
Knowledge of data ingestion patterns (e.g., Datafusion or Dataflow)
Proven experience in production support, covering Incidents, Change Requests (CRs), and Problem Management (PRBs)
Desirable skills/knowledge/experience
AI , Machine Learning
Experience with monitoring tools like Dynatrace and SPLUNK
- Build and maintain data pipelines on GCP using BigQuery, Dataflow and Composer/Airflow.
- Write SQL and Python/PySpark code for data engineering and automation.
- Create modular, reusable, testable data transformations using DBT.
- Monitor and troubleshoot production data pipelines.
- Handle incidents, change requests and problem management for data services.
- Strong hands-on experience with GCP: BigQuery, Dataflow, Composer/Airflow, Cloud Storage, Cloud Run.
- Proficiency in SQL and Python/PySpark for data engineering.
- Hands-on DBT experience for data transformation and modelling.
- Experience with client schedulers (e.g., TWSD) and cloud-native orchestration.
- Proven production support experience covering incidents, CRs and problem management.