Technical Role :

- Design, build, and maintain scalable data pipelines to ingest, process, transform, and deliver large volumes of structured and unstructured data.

- Develop and optimize ETL/ELT frameworks to support data integration, reporting, analytics, and business intelligence initiatives.

- Build and maintain Data Lake, Data Warehouse, and Data Mart solutions to support enterprise data consumption.

- Implement automated data quality checks, validation controls, monitoring, and reporting to ensure data accuracy, consistency, and reliability.

- Design and manage batch processing workflows for large-scale data processing and transformation.

- Collaborate with business stakeholders, analytics teams, and technology partners to understand data requirements and deliver scalable solutions.

- Optimize data pipelines and storage architectures for performance, scalability, and cost efficiency.

- Ensure compliance with data governance, security, and regulatory standards.

- Support data modeling activities including fact and dimension design, data cataloging, and metadata management.

- Troubleshoot production data issues and perform root cause analysis to maintain platform reliability.

Tech stack to look for in profile :

1. Python/PySpark

2. SQL

3. Data lake, Data Warehouse, Data Mart

4. AWS : EMR, Glue, S3, Data Catalog, Redshift

5. Airflow

6. Batch processing