Data Engineer

Experience Required-3-7years

What You’ll Do

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows to ingest, transform, and process large volumes of structured and semi-structured data.

• Develop and optimize data models, tables, and transformations to support analytics, reporting, and downstream data consumption.

• Work with large datasets using SQL, PySpark, and modern data platforms such as Snowflake and Databricks to ensure efficient data processing.

• Build and manage data workflows using orchestration tools such as Apache Airflow, ensuring reliable and timely data delivery.

• Develop automation scripts using Shell Scripting and Python to support data pipeline execution, monitoring, and operational efficiency.

• Monitor, troubleshoot, and optimize data pipelines to improve performance, scalability, and reliability across the data ecosystem.

• Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and enable data-driven insights

• Ensure adherence to data engineering best practices, including data quality checks, documentation, and pipeline governance.

What We’re Looking For

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.

• 3–8 years of experience in data engineering or data platform development within large-scale data environments.

• Strong proficiency in SQL, Python, and distributed data processing frameworks such as PySpark.

• Hands-on experience with modern data platforms such as Snowflake and Databricks.

• Experience building and managing workflow orchestration pipelines using Apache Airflow.

• Exposure to Shell Scripting for automation and operational workflow management.

• Strong understanding of data modeling, ETL/ELT processes, and scalable data pipeline architecture.

• Ability to collaborate effectively with cross-functional teams and global stakeholders.

Nice to Have

• Experience working with cloud platforms such as AWS, GCP, or Azure.

• Familiarity with data lakehouse architectures, data governance, and modern data platform practices.

• Proven ability to work with global stakeholders in cross-functional, matrixed environments.