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.