Senior Data Engineer

Key Responsibilities

- Design, develop, and maintain scalable ETL pipelines for structured and unstructured data

.- Work with Python and PySpark to build efficient data processing frameworks.

- Implement data ingestion, transformation, and integration workflows across multiple systems.

- Leverage AWS Cloud services (S3, Glue, EMR, Redshift, Lambda, Athena, etc.) for data engineering solutions.

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

- Ensure data quality, integrity, and governance across all data processes.

- Collaborate with data scientists, analysts, and business stakeholders to provide reliable data solutions.

- Troubleshoot, debug, and resolve performance bottlenecks in data workflows.

- Implement CI/CD practices and contribute to automation in data engineering processes.

- Stay updated on emerging cloud-native and big data technologies to recommend improvements.

Required Skills & Experience-

5.5 - 8 years of experience as a Data Engineer or in a similar role.

- Strong proficiency in Python programming for data engineering.

- Hands-on experience with PySpark for big data processing.

- Solid expertise in ETL development and data pipeline automation.

- In-depth experience with AWS Cloud services (S3, Glue, EMR, Redshift, Lambda, Athena, CloudWatch).

- Strong knowledge of SQL and relational databases.

- Good understanding of data warehousing concepts and performance optimization

.- Familiarity with CI/CD pipelines, Git, and DevOps practices for data workflows

.- Excellent problem-solving skills and ability to work independently as well as in a collaborative environment.

- Strong communication skills to work with global stakeholders.

Preferred Skills-

Experience with Kafka / Kinesis for real-time streaming.

Exposure to Docker / Kubernetes for data pipeline deployment.

- Knowledge of data governance, security, and compliance standards