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Bengaluru location, popular Data Engineer title, and broad tech requirements increase applicant density.
Core cloud and data engineering skills (PySpark, Kafka, Snowflake, Airflow) are highly transferable across industries.
Explicit 8+ years requirement plus many mandatory technologies and platforms raises filtering rigor.
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Own end-to-end delivery of scalable, reliable batch and streaming data pipelines on an AWS-based cloud data platform using Lakehouse and warehousing technologies.
Design and optimize data architectures for performance, security, cost, and governance, leveraging tools like Kafka, Airflow, Snowflake, and Iceberg.
Lead technical decisions, mentor engineers, improve code quality, and partner with product and analytics teams to translate business needs into data engineering solutions.
8+ years total experience with at least 6 years in data engineering.
Strong expertise in AWS data engineering including hands-on with AWS Glue, Lambda, S3, IAM, and SageMaker.
Proficient in building batch and streaming data pipelines using Kafka, Airflow, SQL, Python, and PySpark.
Experience with Snowflake, CI/CD, Terraform, and data modeling for large-scale data systems.
Experienced senior-level data engineer comfortable leading technical decisions on cloud data platforms and pipeline architectures.
Skilled in both batch and real-time data processing with a strong focus on operational excellence including SLAs and cost-performance optimization.
Able to collaborate effectively across product, analytics, and platform teams, and mentor junior engineers to elevate engineering standards.