Job Description

About Oracle FSGIU - Finergy:

Finergy division within Oracle FSGIU exclusively focuses on the Banking, Financial Services, and Insurance (BFSI) sector, offering deep domain knowledge to address complex financial needs. Finergy has Industry expertise in BFSI. ( On Accelerated Implementation) Finergy has Proven methodologies that fast-track the deployment of multi-channel delivery platforms, minimizing IT intervention and reducing time to market. Due to Personalization tools that tailor customer experiences, Finergy has several loyal customers for over a decade. ( On End-to-End Banking Solutions) Finergy Provides a single platform for a wide range of banking services—trade, treasury, cash management—enhancing operational efficiency with integrated dashboards and analytics. Finergy offers Expert Consulting Services, Comprehensive consulting support, from strategy development to solution implementation, ensuring the alignment of technology with business goals.

Job Responsibilities

• Design, develop, and optimize data processing pipelines using PySpark on AWS Databricks, ensuring scalability and high performance.
• Implement data ingestion, transformation, and aggregation workflows to support advanced analytics and enterprise reporting needs.
• Collaborate with cross-functional teams to build cloud-native data solutions leveraging AWS services such as S3 and EC2
• Write efficient Python code for automation, orchestration, and integration of data workflows across environments.
• Ensure data quality, reliability, and governance through the implementation of best practices, including error handling, auditing, and logging.
• Participate in performance tuning of PySpark jobs, cloud cost optimization, and continuous improvement of the data platform.

Mandatory Skills:

• 4 to 8 years of professional and relevant experience in software industry.
• Strong hands-on expertise in PySpark for distributed data processing and transformation.
• Proven experience with Python programming, including implementing reusable, production-grade code.
• Practical knowledge of AWS Databricks for building and orchestrating large-scale data pipelines.
• Demonstrated experience in processing structured and unstructured data using Spark clusters and cloud data platforms.
• Ability to apply data engineering best practices including version control, CI/CD for data pipelines, and performance tuning.

Preferred Skills:

• Working knowledge of SQL for data querying, analysis, and troubleshooting.
• Experience using AWS S3 for object storage and EC2 for compute orchestration in cloud environments.
• Understanding of cloud-native data architectures and principles of data security and governance.
• Exposure to BFSI domain use cases and familiarity with handling sensitive financial data.
• Familiarity with CI/CD tools and cloud-native deployment practices.
• Familiarity with ETL scripting and data pipeline automation.
• Knowledge of big data ecosystems and distributed computing.

About Oracle FSGIU - Finergy:

Finergy division within Oracle FSGIU exclusively focuses on the Banking, Financial Services, and Insurance (BFSI) sector, offering deep domain knowledge to address complex financial needs. Finergy has Industry expertise in BFSI. ( On Accelerated Implementation) Finergy has Proven methodologies that fast-track the deployment of multi-channel delivery platforms, minimizing IT intervention and reducing time to market. Due to Personalization tools that tailor customer experiences, Finergy has several loyal customers for over a decade. ( On End-to-End Banking Solutions) Finergy Provides a single platform for a wide range of banking services—trade, treasury, cash management—enhancing operational efficiency with integrated dashboards and analytics. Finergy offers Expert Consulting Services, Comprehensive consulting support, from strategy development to solution implementation, ensuring the alignment of technology with business goals.

Job Responsibilities

• Design, develop, and optimize data processing pipelines using PySpark on AWS Databricks, ensuring scalability and high performance.
• Implement data ingestion, transformation, and aggregation workflows to support advanced analytics and enterprise reporting needs.
• Collaborate with cross-functional teams to build cloud-native data solutions leveraging AWS services such as S3 and EC2
• Write efficient Python code for automation, orchestration, and integration of data workflows across environments.
• Ensure data quality, reliability, and governance through the implementation of best practices, including error handling, auditing, and logging.
• Participate in performance tuning of PySpark jobs, cloud cost optimization, and continuous improvement of the data platform.

Mandatory Skills:

• 4 to 8 years of professional and relevant experience in software industry.
• Strong hands-on expertise in PySpark for distributed data processing and transformation.
• Proven experience with Python programming, including implementing reusable, production-grade code.
• Practical knowledge of AWS Databricks for building and orchestrating large-scale data pipelines.
• Demonstrated experience in processing structured and unstructured data using Spark clusters and cloud data platforms.
• Ability to apply data engineering best practices including version control, CI/CD for data pipelines, and performance tuning.

Preferred Skills:

• Working knowledge of SQL for data querying, analysis, and troubleshooting.
• Experience using AWS S3 for object storage and EC2 for compute orchestration in cloud environments.
• Understanding of cloud-native data architectures and principles of data security and governance.
• Exposure to BFSI domain use cases and familiarity with handling sensitive financial data.
• Familiarity with CI/CD tools and cloud-native deployment practices.
• Familiarity with ETL scripting and data pipeline automation.
• Knowledge of big data ecosystems and distributed computing.

Career Level - IC2

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