Senior Data Engineer

Key Responsibilities • Define and own enterprise data warehouse (EDW) architecture across Bronze, Silver and Gold layers. • Design and develop scalable, metadata-driven ETL/ELT frameworks and reusable engineering accelerators. • Lead large-scale data integration and pipeline development across Teradata, Hadoop/Cloudera and Spark platforms. • Establish engineering standards for data modelling, SCD, CDC, historical data management and data recovery. • Implement data lineage, data quality, reconciliation, auditability and governance controls. • Optimize complex SQL queries, Spark workloads, MPP architecture, partitioning and performance. • Develop enterprise banking data solutions, payment harmonisation and business data products. • Automate data engineering deployment and infrastructure using Terraform and CI/CD pipelines. • Lead technical design reviews, code reviews and architecture decisions to ensure scalable solutions. • Mentor engineering teams and collaborate with architects, business analysts, testing, DevOps and operations teams. Requirements • Bachelor's degree in Computer Science, Information Technology, Engineering or a related discipline. • Minimum 10+ years of experience in Data Engineering, including at least 5 years in enterprise data warehouse architecture and development. • Strong hands-on experience with Teradata or other MPP databases such as Netezza, Greenplum, Exadata or IBM Db2 Warehouse. • Strong understanding of enterprise EDW architecture, including Bronze, Silver and Gold layered architecture. • Hands-on experience with Informatica and enterprise data integration frameworks. • Strong technical knowledge of Hadoop, Cloudera, Spark, Hive, Impala and advanced SQL. • Strong programming skills in Python and/or Java/Scala, with experience developing reusable engineering frameworks. • Experience in metadata-driven ETL automation, data modelling, SCD, CDC, data quality and reconciliation frameworks. • Banking or financial services domain experience, preferably with Teradata FSLDM, IBM BDW or equivalent banking data models. • Exposure to Databricks, Kafka, Airflow, Terraform, Kubernetes, Git and CI/CD pipelines will be an added advantage.