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Popular data-engineer role in a metro location with a broad modern stack increases competition density.
Core data engineering and cloud platform skills are highly transferable across industries.
Multiple mandatory technologies (Databricks, PySpark, Snowflake, Kafka) enforce rigorous technical filtering.
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Design and implement scalable batch and streaming data pipelines using PySpark, Apache Kafka, and Snowflake to support enterprise-scale data platforms.
Architect and enforce data engineering solutions aligned with Lakehouse principles, including workflow orchestration with Apache Airflow or Databricks Workflows, and ensure data quality, validation, and governance.
Lead troubleshooting of complex data processing issues, mentor team members on best practices, and collaborate with stakeholders for end-to-end data platform delivery.
Proficient with Databricks Workflows, PySpark, Apache Kafka, and Snowflake.
Experience with modern distributed data processing frameworks and event-driven architectures (Kafka or Amazon Kinesis).
Work Experience Required: Not explicitly mentioned in the JD.
Location: Noida, UP, India.
Experienced in designing and optimizing large-scale data platforms and pipelines in enterprise environments.
Able to balance scalability, performance, reliability, and business priorities while promoting data quality and engineering excellence.
Operates effectively within collaborative teams, driving innovation through adoption of modern data engineering and AI-assisted development practices.