AWS Databricks Engineer

Experience: 5-8 Years

Location: Pune (Hinjewadi), Mumbai (Mensa), Chennai, Hyderabad (Metro Campus), Bengaluru (Whitefield)

Notice Period: Immediate to 30 Days

Job Summary

We are looking for experienced AWS Databricks Engineers with solid hands-on expertise in AWS, Databricks, PySpark, and SQL to develop, maintain, and optimize scalable data engineering solutions. The candidate will be responsible for building data pipelines, transforming large datasets, and delivering reliable data solutions in an enterprise environment.

Key Responsibilities

- Design, develop, and maintain data pipelines using Databricks and PySpark.
- Build and optimize ETL/ELT workflows on AWS cloud platforms.
- Write complex SQL queries for data extraction, transformation, and validation.
- Develop data transformation logic using PySpark and Spark SQL.
- Work with AWS services to ingest, process, store, and manage data.
- Optimize Spark jobs and data pipelines for performance and scalability.
- Perform data quality checks, validation, and troubleshooting.




- Monitor pipeline execution and resolve data processing issues.
- Collaborate with data engineers, architects, and business stakeholders.
- Follow data engineering best practices for security, reliability, and maintainability.

Mandatory Skills

- AWS
- Databricks
- PySpark
- SQL
- Data Engineering
- ETL/ELT Data Pipelines
- Apache Spark

Candidate Requirements

- 58 years of experience for candidates with a technical educational background.
- 69 years of experience for candidates with a non-technical educational background.
- Strong hands-on experience with AWS, Databricks, PySpark, and SQL.
- Good analytical, troubleshooting, and problem-solving skills.
- Ability to work in a collaborative enterprise environment.

Interview Drive Details

- Virtual drive: Saturday, 31 October 2026.
- Profile submission: 30+ profiles required on priority.

- Joining: Immediate to 30 days preferred.

📌 AWS Databricks Engineer (Hyderabad)
🏢 Advance Career Solutions
📍 Hyderabad