Python AWS Developer

Delivery Centric Technologies; Pune District, Chennai District, Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Delivery Centric Technologies in Pune, India, seeks a senior data engineer to design and optimize scalable data pipelines using PySpark on AWS Databricks for analytics and reporting.

You will collaborate with cross-functional teams to build cloud-native data solutions on AWS and write production-grade Python for automation and orchestration of data workflows. Emphasis on data quality, governance, and cost optimization across environments.

Qualifications

• 6+ 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.

Responsibilities

• Design, develop, and optimize data pipelines using PySpark on AWS Databricks.
• 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.

Skills

PySpark

Python

CI/CD for data pipelines

Data governance

Performance tuning

Tools

AWS Databricks

Spark clusters

SQL

Job description

Role & 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

• 6+ 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