Key Responsibilities:

- Design, develop, and maintain efficient, reliable, and scalable data pipelines using modern data engineering tools and best practices.

- Build and optimize data flows between internal and external data sources and destinations.

- Implement data quality checks, monitoring, and alerting mechanisms.

- Write clean, efficient, maintainable, and production-ready code.

- Follow coding standards and engineering best practices.

- Participate in code reviews and contribute to technical documentation.

- Troubleshoot and resolve data pipeline failures, data quality issues, and performance bottlenecks.

- Collaborate with cross-functional teams to support analytics and data-driven business initiatives.

Required Technical Skills:

- Strong proficiency in SQL and data modelling.

- Hands-on experience with Python or Java.

- Proven experience with Apache Spark for large-scale data processing.

- Strong experience with Apache Airflow for workflow orchestration.

- Experience with modern data platforms and cloud data warehouses, particularly Databricks and/or Snowflake.

- Experience with at least one major cloud platform: AWS, GCP, or Azure.

- Strong understanding of data engineering concepts, ETL processes, and data warehousing principles.

- Good understanding of scalable data pipeline architecture and performance optimization.

Good to Have:

- Databricks certification.

- Snowflake certification.

- Experience working across multiple cloud platforms.

- Exposure to modern data engineering and analytics ecosystems.