Python Developer with AWS

About Oracle FSGIU - Finergy:

On Accelerated Implementation

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.