Leads design and delivery of large-scale data engineering in banking environments: building ETL pipelines and data platform migrations (Teradata to Hadoop/Data Lake) using Python, PySpark, SQL, Spark, Hive, and Teradata, while driving regulatory/risk-data initiatives (AML, MAS, BCBS239) and providing technical leadership to data engineering teams.
Job Summary
Lead the design and implementation of large-scale data engineering solutions within banking environments. Drive data platform modernization, migration, and regulatory initiatives while managing technical teams and collaborating with stakeholders to ensure robust, high-quality data delivery.
Responsibilities
Design and implement large-scale data pipelines, ETL frameworks, and batch processing solutions to support enterprise data platforms.
Develop and optimize data integration processes using Python, PySpark, SQL, Hadoop HDFS, Spark, Hive, and Teradata technologies.
Lead Teradata-to-Hadoop/Data Lake migration projects, legacy platform modernization, technology refresh, and application decommissioning efforts.
Manage and optimize enterprise ETL platforms such as Talend, DataStage, or Informatica, including migration and performance tuning.
Apply data modeling, profiling, quality assurance, lineage tracking, and transformation techniques on large structured and unstructured datasets.
Provide technical leadership to large data engineering teams through solution design, code and design reviews, troubleshooting, delivery governance, and production support.
Design data architecture frameworks, CI/CD pipelines, and orchestration workflows using Autosys, Control-M, Jenkins, and Git.
Oversee enterprise data migration, job optimization, capacity planning, and production/BAU support in high-volume banking environments.
Deliver regulatory and risk-data initiatives aligned with AML, Fraud, MAS regulations, BCBS239, data quality, and critical data element lineage requirements.
Manage stakeholder relationships with banking technology teams, business analysts, infrastructure, data management, and senior clients to ensure successful technical solution delivery.
Required competencies and certifications
Hands-on expertise in Python, PySpark, SQL, Hadoop HDFS, Spark, Hive, and Teradata technologies.
Experience with enterprise ETL platforms such as Talend, DataStage, or Informatica.
Proven technical leadership of large data engineering teams.
Experience in Teradata-to-Hadoop/Data Lake migration and legacy platform modernization.
Experience delivering regulatory or risk-data initiatives including AML, Fraud, MAS regulatory requirements, BCBS239, and data quality.
Preferred competencies and qualifications
Experience within Banking/Financial Services, especially Retail Banking, Cards, AML, Fraud, Regulatory Reporting, or Data Warehouse environments.
Strong stakeholder management skills with cross-functional teams and senior client stakeholders.
Experience designing CI/CD pipelines and orchestration using Autosys, Control-M, Jenkins, and Git.
Experience with performance tuning, job optimization, capacity planning, and production/BAU support in high-volume banking environments.