Senior Data Engineer – Banking/Financial
Must-have:PythonGitDataCI/CDFinTechE-CommerceSenior
- 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.