Data Engineer
Virtusa
Date: 2 weeks ago
City: Chennai, Tamil Nadu
Contract type: Full time
We are looking for a highly skilled Senior Data Engineer to join our team.
The ideal candidate will have hands-on experience working with large-scale data platforms and a strong background in Python, PySpark, AWS, and modern data warehousing tools such as Snowflake and DBT. Familiarity with NoSQL databases like MongoDB and real-time streaming platforms like Kafka is essential.
Key Responsibilities
Design, build, and maintain scalable data pipelines using PySpark and Python.
Work with AWS cloud services (S3, Lambda, Glue, EMR, Redshift) for data ingestion, processing, and storage.
Implement and maintain ETL workflows using DBT and orchestration tools (e.g., Airflow).
Design and manage data models in Snowflake, ensuring performance and reliability.
Work with SQL for querying and optimizing datasets across different databases.
Integrate and manage data from MongoDB, Kafka, and other streaming or NoSQL sources.
Collaborate with data scientists, analysts, and other engineers to support advanced analytics and ML initiatives.
Ensure data quality, lineage, and governance through best practices and tools.
Required Skills & Qualifications
Strong programming skills in Python and PySpark.
Hands-on experience with AWS data services.
Proficiency in SQL and experience with DBT for data transformation.
Experience with Snowflake for data warehousing.
Knowledge of MongoDB, Kafka, and data streaming concepts.
Good understanding of data architecture, modeling, and data governance.
Experience with CI/CD and DevOps practices in a data engineering environment is a plus.
Excellent problem-solving skills and the ability to work independently or as part of a team.
The ideal candidate will have hands-on experience working with large-scale data platforms and a strong background in Python, PySpark, AWS, and modern data warehousing tools such as Snowflake and DBT. Familiarity with NoSQL databases like MongoDB and real-time streaming platforms like Kafka is essential.
Key Responsibilities
Design, build, and maintain scalable data pipelines using PySpark and Python.
Work with AWS cloud services (S3, Lambda, Glue, EMR, Redshift) for data ingestion, processing, and storage.
Implement and maintain ETL workflows using DBT and orchestration tools (e.g., Airflow).
Design and manage data models in Snowflake, ensuring performance and reliability.
Work with SQL for querying and optimizing datasets across different databases.
Integrate and manage data from MongoDB, Kafka, and other streaming or NoSQL sources.
Collaborate with data scientists, analysts, and other engineers to support advanced analytics and ML initiatives.
Ensure data quality, lineage, and governance through best practices and tools.
Required Skills & Qualifications
Strong programming skills in Python and PySpark.
Hands-on experience with AWS data services.
Proficiency in SQL and experience with DBT for data transformation.
Experience with Snowflake for data warehousing.
Knowledge of MongoDB, Kafka, and data streaming concepts.
Good understanding of data architecture, modeling, and data governance.
Experience with CI/CD and DevOps practices in a data engineering environment is a plus.
Excellent problem-solving skills and the ability to work independently or as part of a team.
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