Job Description

.15+ years of hands-on experience in Data Engineering, Data Warehousing, ETL and enterprise data platforms, preferably within Banking/Financial Services.

.Strong hands-on expertise in Python, PySpark, SQL, Hadoop HDFS, Spark, Hive and Teradata candidates without these core technologies will not be considered.

.Proven experience designing and implementing large-scale data pipelines, ETL frameworks, data integration and batch processing solutions.

.Extensive experience with at least one enterprise ETL platform such as Talend, DataStage or Informatica, including migration and optimisation.

.Strong experience in Teradata-to-Hadoop/Data Lake migration, legacy platform modernisation, technology refresh and application decommissioning.

.Experience working on enterprise Banking data platforms, preferably with Retail Banking, Cards, AML, Fraud, Regulatory Reporting or Data Warehouse environments.

.Hands-on experience with data modelling, data profiling, data quality, data lineage, data transformation and large-volume structured/unstructured datasets.

.Proven technical leadership of large data engineering teams, including solution design, code/design reviews, technical troubleshooting, delivery governance and production support.

.Strong experience designing data architecture/frameworks, CI/CD pipelines, orchestration and batch processing using tools such as Autosys, Control-M, Jenkins and Git.

.Experience with enterprise data migration, performance tuning, job optimisation, capacity planning and production/BAU support in high-volume banking environments.

.Experience delivering regulatory or risk-data initiatives, such as AML, Fraud, MAS regulatory requirements, BCBS239, data quality or critical data element lineage.

.Strong stakeholder management with Banking technology teams, Business Analysts, Infrastructure teams, Data Management teams and senior client stakeholders, with ownership of technical solution delivery.