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Popular mid-level Data Engineer role in a metro with common skills, increasing applicant competition.
Core data engineering skills are transferable, but GCP/Databricks platform focus increases sensitivity.
Mandatory specific stack (PySpark, Kafka, Delta Lake, Airflow) increases screening despite no explicit years.
Design and implement scalable data engineering solutions using PySpark, Apache Kafka, Delta Lake on Databricks, and Apache Airflow.
Lead development of batch and streaming data pipelines and optimize event-driven data architectures for enterprise-scale platforms.
Drive data quality, governance, and operational controls while mentoring team members and collaborating with stakeholders for end-to-end data platform delivery.
Mandatory skills include Apache Airflow, PySpark, Apache Kafka, Delta Lake on Databricks.
Experience with Snowflake or equivalent enterprise-scale data platforms is required.
Work Experience Required: Not explicitly mentioned in the JD.
Must be located in Noida, Uttar Pradesh, India (Location specified).
Strong ownership mentality driving data engineering excellence with focus on scalability, reliability, and performance.
Experienced in leading implementation of modern Lakehouse architectures and event-driven streaming data solutions.
Skilled in mentoring and knowledge sharing on advanced data engineering tools and best practices within collaborative teams.