Job summary
We are seeking a highly skilled and motivated senior data engineer to design, develop, and optimize scalable data platforms and pipelines within the banking and financial services domain. The ideal candidate will have strong expertise in Python, SQL, big data technologies, and cloud-based data ecosystems. You will be responsible for building robust, secure, and scalable data solutions that support regulatory reporting, risk management, customer analytics, and business intelligence initiatives.
Key responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines for processing large volumes of banking and financial data.
- Build and optimize data workflows using Apache Spark and Apache Airflow.
- Design and implement data warehouse and data lake architectures to support enterprise banking analytics and reporting requirements.
- Develop and maintain high-performance data models for transactional, customer, risk, and regulatory data.
- Collaborate with business stakeholders, data analysts, risk teams, and compliance teams to deliver data-driven solutions.
- Ensure data quality, governance, security, and regulatory compliance across data platforms.
- Implement monitoring, troubleshooting, and performance tuning for data pipelines and processing frameworks.
- Leverage cloud platforms or Cloudera ecosystem technologies to build and manage modern data infrastructure.
- Drive CI/CD implementation and DataOps best practices to improve deployment efficiency and operational reliability.
- Support regulatory reporting, audit requirements, and data lineage initiatives.
- Mentor junior engineers and contribute to technical architecture and best practices.
Requirements
Required qualifications
- Bachelor's degree in computer science, information technology, engineering, or a related field.
- 4+ years of hands-on experience in data engineering.
- Strong programming skills in Python and advanced SQL.
- Experience with Apache Spark for large-scale data processing.
- Experience with Apache Airflow for workflow orchestration and scheduling.
- Solid understanding of data warehouse and data lake architectures.
- Proven experience building and maintaining ETL/ELT pipelines.
- Experience working with cloud platforms (AWS, Azure, or GCP) or the Cloudera ecosystem.
- Strong knowledge of data modeling techniques and performance optimization strategies.
- Experience implementing CI/CD pipelines and DataOps methodologies.
- Banking or financial services domain experience with exposure to financial data, regulatory reporting, risk, compliance, or customer analytics.
- Excellent problem-solving, analytical, and communication skills.
Preferred qualifications
- Experience with banking data domains such as core banking, payments, lending, treasury, risk, AML, KYC, or regulatory reporting.
- Knowledge of financial data governance, security, and compliance standards.
- Experience with real-time and batch data processing in highly regulated environments.
- Familiarity with containerization technologies such as Docker and Kubernetes.
- Experience with data lineage, metadata management, and data quality frameworks.