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Specialized Databricks/PySpark/Kafka requirements and metro location create moderate competition.
Core data engineering skills like PySpark, Kafka, and Databricks transfer easily across industries.
Multiple mandatory technologies (Databricks, PySpark, Kafka, Snowflake) and seniority make screening strict.
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Design and implement scalable batch and streaming data pipelines using PySpark, Delta Lake on Databricks, and Apache Kafka to support enterprise-scale data platforms.
Architect and optimize data ingestion, transformation, and event-driven data architectures emphasizing Lakehouse principles and platform engineering standards.
Lead workflow orchestration and operational monitoring for reliable pipeline execution, enforce data quality and governance, and mentor teams on best practices across relevant technologies.
Mandatory skills: Databricks Workflows, PySpark, Delta Lake on Databricks, Apache Kafka.
Experience designing and implementing high-performance batch and streaming data pipelines.
Work Experience Required: Not explicitly mentioned in the JD.
Location: Noida, UP, India.
Experienced data engineer skilled in designing scalable distributed data processing solutions and modern Lakehouse architectures.
Proficient in multiple data engineering frameworks and platforms including Snowflake, Delta Lake, Apache Kafka/Kinesis, and orchestration tools like Airflow or Databricks Workflows.
Capable of leading end-to-end data platform delivery, balancing performance, scalability, reliability with business needs, and mentoring peers on advanced data engineering practices.