Data Engineer

About the Role: We are looking for an experienced Data Engineer to design, build, and operate the pipelines that power our Databricks-based Prism platform. This role is for someone who has moved beyond one-off scripts and hand-built pipelines — you've built ingestion and transformation frameworks that are driven by configuration, not bespoke code, and you can stand up a new data source by writing a config file rather than a new pipeline from scratch. Key Responsibilities Design and develop scalable, metadata-driven data pipelines. Build and maintain ingestion frameworks supporting batch, CDC, streaming, API, and file-based data sources. Develop transformation layers using dbt, including testing, documentation, and reusable components. Work closely with data architects and product owners to translate business requirements into data solutions. Implement governance and lineage using Unity Catalog. Build CI/CD pipelines and ensure high data quality, observability, and operational reliability. Troubleshoot and optimise end-to-end data workflows. Required Skills & Experience 3+ years of Data Engineering experience in production environments. Strong experience with configuration-driven or metadata-driven data pipeline frameworks. Hands-on experience with dbt, including model development, testing, and documentation. Strong SQL and Python skills. Experience with cloud data platforms such as Databricks or Snowflake. Knowledge of orchestration tools such as Airflow, Databricks Workflows, or dbt Cloud. Ability to explain architecture decisions, troubleshooting approaches, and pipeline design best practices. Preferred Qualifications Databricks, Delta Lake, and Unity Catalog experience. Terraform and Git-based CI/CD practices. Experience with Kafka, Structured Streaming, or Event Hubs. Background in Financial Services or Lending domains. Experience migrating legacy ETL solutions to cloud-based architectures. #J-18808-Ljbffr