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Mid-level data engineer in Bangalore, common title and metro location create high competition.
Core data engineering skills are widely transferable across industries despite optional telecom domain preference.
Explicit 4–7 years plus mandatory AWS, DBT, Redshift, and PySpark requirements increase selection strictness.
Design, build, and optimize large-scale AWS data engineering solutions focusing on Redshift, Lakehouse architectures with Apache Iceberg, and scalable batch/streaming pipelines.
Develop and maintain data pipelines using DBT (Cloud+Core), Spark/PySpark, Python, and SQL with focus on performance tuning and production support including monitoring, logging, and alerting.
Implement and optimize data warehouse and analytics systems involving AWS services like S3, Athena, Glue, EMR, Aurora PostgreSQL, and manage CI/CD and data engineering best practices.
4-7 years of professional experience in data engineering, specifically with large-scale AWS data solutions.
3+ years hands-on experience with DBT (Cloud+Core) required.
Strong expertise in AWS services including Redshift, S3, Athena, Glue, EMR, Aurora PostgreSQL, Lambda and others as listed; plus Spark/PySpark, Python, SQL (advanced functions).
Location must be Mumbai or Bangalore; notice period Not explicitly mentioned in the JD.
Experienced with performance tuning and optimization for Spark/PySpark, Apache Iceberg, and Aurora PostgreSQL to handle scalable data workloads.
Capable of designing Lakehouse/Data Lake architectures with strong understanding of data modeling, partitioning, and file formats in cloud environments.
Familiarity with AWS certification or equivalent, and advantage if skilled in Terraform, Kafka, GenAI dev tools, telecom domain, and container technologies like Docker/Kubernetes.