Data Engineer

End Date Friday 30 October 2026 We Support Flexible Working – Click here for more information on flexible working options Flexible Working Options Hybrid Working Job Description Summary Role Title: Data Engineer Location: Hyderabad Role Type: Hybrid About This Opportunity Are you eager to join a team in the midst of an exciting transformation?

At Lloyds Banking Group, we are redefining the way we harness data to create meaningful outcomes for customers and colleagues. Our multidisciplinary teams place technology, data and engineering at the heart of the business, developing modern products and services in a collaborative and forward-thinking environment. Role Overview We are seeking a Data Engineer to join our Corporate & Institutional Banking (CIB) business.

The role will design, build and support trusted cloud data products and scalable data pipelines for Trade Finance, Finance, Risk, customer experience, reporting and analytics use cases. Job Description Key Responsibilities Design, develop and maintain reusable data products, ingestion pipelines and transformation frameworks on Google Cloud Platform.

Develop and optimise SQL, Python, Py. Spark and dbt transformations supporting batch and large-scale data processing. Build and operate orchestration workflows using Cloud Composer / Apache Airflow and processing solutions using Big. Query, Dataproc and Cloud Storage. Implement data-quality controls covering schema validation, completeness, duplication, reconciliation, business-date alignment and exception handling.

Apply engineering standards through version control, peer review, unit testing, automated deployment and CI/CD practices. Monitor production pipelines, investigate failures and improve reliability, performance, recovery and operational support. Work with Product Owners, Business Analysts, Data Modelers, Architects, BI colleagues and Quality Engineers to deliver end-to-end outcomes.

Produce clear technical designs, data lineage, runbooks and support documentation.

What You'll Need

Strong hands-on experience with GCP, Big. Query, Cloud Storage, Dataproc and Cloud Composer / Apache Airflow. Advanced SQL and practical experience with Python, Py. Spark and dbt. Experience building scalable ETL / ELT pipelines, reusable data products and governed data layers. Knowledge of Git, CI/CD, automated testing, infrastructure-as-code and Terraform.

Understanding of data warehousing, dimensional models, metadata, lineage and data-quality controls. Experience with performance tuning, monitoring, incident investigation and production support. Knowledge of secure cloud engineering, access controls, encryption and regulated-data handling. Strong analytical, documentation and stakeholder communication skills.