Lead Data Engineer - ETL/Python

Description :

About The Role :

We are seeking a highly skilled and experienced Senior / Lead Data Engineer to design, develop, and maintain scalable, reliable, and efficient data pipelines and ETL solutions.

The role requires strong expertise across multi-cloud environments, modern data warehousing platforms, programming languages, and data orchestration tools.

You will play a pivotal role in transforming raw data into actionable insights, ensuring data quality, and enabling analytics and reporting initiatives across the organization.

Responsibilities :

- Design, build, and optimize complex ETL/ELT data pipelines using Python, PySpark, Scala, and advanced SQL.

- Implement and manage ETL processes using Informatica PowerCenter, Databricks, AWS Glue, and Snowflake.

- Develop and deploy scalable data solutions across AWS, Azure, GCP, and Microsoft Fabric using cloud-native services.

- Manage and optimize databases, including Redshift, SQL Server, and AWS RDS.

- Orchestrate and monitor data workflows with Apache Airflow to ensure reliable and timely delivery.

- Implement streaming solutions with Apache Kafka and containerized services with Kubernetes.

- Automate data workflows and system monitoring using Unix shell scripting.

- Apply CI/CD practices to data pipelines and enforce Data Cleanroom principles for privacy-compliant collaboration.

- Collaborate with BI/reporting teams to deliver optimized datasets for Tableau, Looker, and Power BI.

- Troubleshoot and resolve performance issues in pipelines and database queries.

- Maintain detailed technical documentation and collaborate closely with cross-functional teams.

Qualifications:

- Bachelor's or master's degree in Computer Science, Engineering, Information Technology, or a related field.

- Experience: 5+ years for Senior Data Engineer, 8+ years for Lead Data Engineer.

- Languages: Proficiency in SQL, Python (including PySpark), Scala, and Unix Shell Scripting.

- ETL Tools: Hands-on experience with Informatica PowerCenter, Databricks, and AWS Glue.

- Data Warehousing: Expertise in Snowflake and Redshift.

- Cloud Platforms: Strong exposure to at least two of AWS, Azure, and GCP; familiarity with Microsoft Fabric.

- Databases: Solid knowledge of Redshift, SQL Server, and AWS RDS.

- Orchestration: Proven experience with Apache Airflow.

- Streaming Containerization: Practical experience with Apache Kafka and Kubernetes.

- Concepts: Working knowledge of CI/CD pipelines and Data Cleanroom practices.

- Reporting Tools: Understanding of data provisioning for Tableau, Looker, or Power BI.

- Strong problem-solving skills, communication ability, and a proactive approach to emerging technologies.