Develop and deliver scalable transformation frameworks across Hadoop platforms by building robust data pipelines, ingestion frameworks, and automation capabilities. Ensure efficient data processing, performance optimization, and reliable integration of enterprise data sources, while supporting data warehouse, enterprise analytics and downstream consumption requirements. This role offers an opportunity to work on large-scale data platforms handling structured, semi-structured, and streaming data across critical banking domains.
Requirements
Data Engineering & Integration
· Design, develop, and maintain scalable data pipelines within the Enterprise Data Lake platform using Cloudera CDP, Hadoop, Spark, Hive, Java, Python, and SQL.
· Develop high-performance batch and near real-time data ingestion frameworks for onboarding enterprise source systems into the Data Lake.
· Develop complex transformation logic using Spark, HiveQL, SQL, and Java-based processing frameworks.
· Design and implement data ingestion patterns supporting structured, semi-structured, and unstructured datasets.
· Build and maintain batch and near real-time data integration solutions.
· Implement source-to-target mappings and ingestion specifications.
Hadoop & Cloudera Platform Development
· Develop and support solutions within the Cloudera Data Platform ecosystem.
· Build and maintain data pipelines leveraging:
· Hadoop HDFS
· Hive
· Impala
· Spark
· Kafka
· YARN, Oozie
· Perform deployment, troubleshooting, and performance tuning of applications running on the Hadoop ecosystem.
· Partner with platform teams to ensure optimal cluster utilization and workload management.
Data Quality & Governance
· Ensure adherence to Data Lake onboarding standards and governance requirements.
· Investigate and resolve data quality issues and production incidents.
· Ensure compliance with enterprise data governance standards.
DevOps & Platform Automation
· Integrate data engineering solutions into CI/CD pipelines using Git, Jenkins, Azure DevOps, or equivalent tools.
· Automate deployment, testing, monitoring, and operational support activities.
· Develop reusable frameworks and utilities to improve engineering productivity and platform scalability.
Production Support & Incident Management
· Provide L3 support for Data Lake application.
· Perform root cause analysis for production incidents and implement permanent fixes.
· Monitor pipeline execution, cluster health, and workload performance.
· Participate in release implementation, deployment validation, and post-production support activities.
· Strong hands on experience with Hadoop ecosystem and data pipeline orchestration.
· Hadoop/HDFS
· Hive
· Spark
· Unix/Linux scripting
· Shell scripting
· ETL/ELT development experience.
· Data modelling fundamentals.
· Performance tuning and optimization.
· Experience in Data Warehousing and Data Lake environments.
· Strong Java / Python competency with experiences.
· Strong SQL development skills.