- 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 qualified 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