Description

Key Skills: Data Engineer, Pyspark, Airflow, Python, Snowflake, ETL, Power BI, AWS Lambda, Redshift, Snowpipe

Roles and Responsibilities:

• Design and develop resilient ETL and ELT pipelines using Snowflake, Python, PySpark and Airflow.
• Build scalable data ingestion and transformation workflows for structured and semi-structured enterprise data.
• Develop data models, curated datasets and reusable data services that support analytics and reporting requirements.
• Implement data quality checks, pipeline monitoring, error handling and operational controls across production workflows.
• Tune SQL queries, Snowflake workloads and data processing jobs to improve reliability, scalability and cost efficiency.

Skills Required:

• 4 - 7 years of relevant experience in data engineering, cloud data platforms or enterprise data warehousing.
• Hands-on experience with Snowflake, ETL, Python, PySpark and Airflow in data engineering environments.
• Strong understanding of data warehouse architecture, data modelling, data structures and SQL-based transformations.
• Experience building scalable backend data pipelines and integrating data from multiple enterprise sources.
• Ability to develop production-grade workflows with orchestration, testing, data quality validation and operational support.

Good to Have:

• Exposure to AWS Lambda, Amazon Redshift, Power BI and Snowpipe.

Education: Bachelor's or postgraduate degree in Computer Science, Data Science, Engineering, Mathematics, Information Systems or a related discipline

Key Skills

Education

Any Graduate