Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Hadoop ecosystem tools (HDFS, Hive, Spark, Sqoop, Kafka).
• Build and optimize ETL/ELT processes to support data ingestion from multiple banking systems.
• Develop and manage big data solutions for structured and unstructured data.
• Collaborate with data analysts, data scientists, and business stakeholders to deliver data solutions.
• Ensure data quality, integrity, and governance aligned with banking and regulatory standards.
• Perform performance tuning and optimization of Hadoop/Spark jobs.
• Implement data security controls to comply with financial regulations (e.g., PCI, SOX).
• Support real-time and batch data processing frameworks.
• Troubleshoot production issues and provide continuous support for data platforms.
• Work with cloud platforms (e.g., AWS, Azure) for modern data solutions.

Required Skills & Qualifications

Technical Skills

• Strong experience with:
• Hadoop ecosystem (HDFS, MapReduce, Hive, HBase)
• Apache Spark (Scala/Python)
• SQL & NoSQL databases
• ETL tools (Informatica, Talend, or similar)
• Kafka or other streaming tools
• Proficiency in programming:
• Python / Java / Scala
• Experience with:
• Data warehousing concepts
• Workflow orchestration tools (Airflow, Oozie)
• Unix/Linux environments
• Knowledge of cloud data platforms (AWS EMR, Azure Data Lake) is a plus

Domain Knowledge

• Understanding of banking and financial services data
• Exposure to risk, compliance, or fraud analytics is preferred

Soft Skills

• Strong problem-solving and analytical abilities
• Excellent communication and collaboration skills
• Ability to work in Agile/Scrum environments

Education & Experience

• Bachelor’s or Master’s degree in:
• Computer Science, Information Technology, or related field
• Typically 5–10 years of experience in data engineering or big data development