- Design, develop, and maintain scalable data pipelines using Databricks, PySpark, and Apache Spark.

- Build robust ETL/ELT workflows for batch and real-time data processing.

- Develop and optimize data transformation pipelines on AWS.

- Work with AWS services including S3, Glue, IAM, Lambda, EMR, and Redshift.

- Design and implement Data Lake/Lakehouse architectures using Delta Lake.

- Collaborate with business stakeholders, data architects, and engineering teams to deliver scalable data solutions.

- Optimize Spark workloads for performance, scalability, and cost efficiency.

- Implement workflow orchestration using Apache Airflow or similar tools.

- Ensure data quality, governance, security, and operational excellence across data platforms.

- Support production environments, troubleshoot issues, and continuously improve system reliability.

- Follow best practices for version control, CI/CD, and data engineering standards.

Required Skills & Experience :

- 9 - 13 years of experience in Data Engineering.

- Strong hands-on expertise in :

1. Databricks

2. PySpark

3. Apache Spark

4. SQL

- Experience designing and developing ETL/ELT pipelines and data transformation workflows.

- Strong experience with AWS services :

1. Amazon S3

2. AWS Glue

3. IAM

4. AWS Lambda

5. Amazon EMR

6. Amazon Redshift

- Good understanding of Data Lake, Lakehouse Architecture, and Delta Lake.

- Experience with workflow orchestration tools such as Apache Airflow.

- Experience with Git, CI/CD pipelines, and production support.

- Strong performance tuning, troubleshooting, and optimization skills.

- Excellent analytical, communication, and stakeholder management skills.

Preferred Skills :

- Experience with Kafka and Spark Streaming.

- Knowledge of Snowflake, Apache Iceberg, and dbt.

- Experience with Terraform and Infrastructure as Code (IaC).

- AWS or Databricks certifications are an added advantage.

- Exposure to cloud-native data platforms and modern analytics architectures.

Key Competencies :

- Databricks & Apache Spark

- PySpark & SQL

- AWS Data Engineering

- ETL/ELT Development

- Data Lake & Lakehouse Architecture

- Delta Lake & Airflow

- Performance Optimization

- CI/CD & Git

- Data Governance & Production Support

- Cross-functional Collaboration