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Mid-level metro Data Engineer role, popular title and remote-friendly increases applicant competition.
Core data engineering skills transfer across industries, but specific tooling and SAP familiarity slightly limit fit.
Explicit 3–7 years plus mandatory AWS Glue, Airflow, Kafka, Python skills create strict filters.
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Design, develop, and maintain scalable data pipelines using AWS Glue, Apache Airflow, Kafka, and SQL for efficient data integration and processing.
Implement data transformation and ETL processes leveraging Python and SQL, ensuring data quality and integrity through testing and validation.
Collaborate with data scientists and cross-functional teams to improve data lifecycle management and implement DataOps practices, while monitoring and optimizing pipeline performance.
3 to 7 years of relevant experience in data engineering.
Strong expertise with AWS Glue, Apache Airflow, Kafka, SQL, and Python.
Experience working with SAP HANA and knowledge of DataOps tools and methodologies.
Location requirement: Gurgaon (Hybrid Work Model).
Experienced in building and optimizing complex data pipelines within cloud environments using AWS services and orchestration tools.
Skilled at collaborating with stakeholders across data science and analytics to deliver actionable and reliable data solutions.
Comfortable working in a hybrid setting with agile, cross-functional teams and adhering to DataOps best practices.