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Senior, niche Databricks data role in Bangalore with non-Tier-1 brand reduces applicant density.
Databricks and data-platform expertise favors data-background candidates but remains moderately transferable across industries.
Explicit 10–14 years plus mandatory Databricks, Spark, cloud, and infra skills enforce strict filters.
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Architect, design, and develop large-scale cloud-native data platforms using Databricks, Spark, PySpark, Python, and AWS.
Build high-performance distributed data processing solutions for massive-scale datasets, including batch and real-time processing.
Lead architectural decisions and establish engineering standards while mentoring engineers and providing technical guidance.
10–14 years of software/data engineering experience with strong hands-on expertise.
Proficiency in Databricks, Python, PySpark, Apache Spark.
Experience with AWS ecosystem services including S3, Glue, Redshift, EMR, Athena, Lambda, or EventBridge.
Experience with Data Lakes, Delta Lake, Data Warehousing, streaming technologies (Kafka/Kinesis/SQS/RabbitMQ), and DevOps tools like Terraform/Ansible and CI/CD.
Senior individual contributor comfortable balancing hands-on engineering and architecture leadership.
Experienced in designing scalable, distributed data platforms working at cloud scale with large datasets.
Capable of influencing technical direction, driving engineering standards, and collaborating across global teams.