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Metro-based, popular data role increases competition, but seniority and niche Databricks/streaming skills limit applicant pool.
Platform-specific Databricks, Delta Lake, PySpark and AWS streaming expertise makes cross-industry transferability limited.
Mandatory Databricks, PySpark, streaming and AWS skills create strict technical filters for shortlisting.
Design and implement scalable data engineering solutions using PySpark, Delta Lake on Databricks, and Amazon Kinesis, including high-performance batch and streaming data pipelines.
Architect and optimize data ingestion, transformation, and workflow orchestration solutions employing Snowflake, Apache Airflow or Databricks Workflows aligned with enterprise-scale data platforms.
Lead data quality, validation, governance, and operational monitoring practices to ensure reliable pipeline execution and drive development of business-focused data products.
Mandatory skills include: PySpark, Amazon Kinesis, Delta Lake on Databricks, Databricks Workflows.
Experience with big data technologies such as Apache Spark, Kafka, Airflow, and AWS cloud services including AWS Lambda, AWS EventBridge, AWS Fargate, AWS SNS, AWS SQS, AWS Glue, AWS EMR, AWS Redshift.
Work Experience Required: Not explicitly mentioned in the JD.
Location: Noida, UP, India (onsite requirement implied).
Experienced in designing and leading enterprise-scale, event-driven, and streaming data architectures using modern distributed data processing frameworks on AWS cloud environment.
Skilled in workflow orchestration and operationalizing production data pipelines with a focus on data quality, observability, and governance.
Capable of mentoring and collaborating effectively across teams to ensure data engineering best practices and continuous improvement are maintained.