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Strong brand, Bangalore metro, mid-level generalist title, and broad toolset create high competition.
Core data engineering skills (ETL, Spark, SQL, cloud) are highly transferable across industries, so sensitivity is low.
Explicit 3–7 years plus many mandatory technologies (Databricks, PySpark, Kafka, AWS, SQL, Airflow) increases strictness.
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Design, develop, and maintain scalable batch and streaming data pipelines using cloud-native platforms like Databricks, Snowflake, and AWS services.
Ensure high data quality, consistency, security, and operational excellence in data engineering solutions powering digital products and analytics.
Collaborate cross-functionally and mentor junior engineers while automating data pipelines and validations end-to-end.
Bachelor's degree in Computer Science, Computer Engineering, or relevant field.
3 to 7 years of experience in architecting, designing, and building Data Engineering solutions and platforms.
Proven expertise with batch and streaming data pipelines using Apache Spark, Kafka, Databricks, Snowflake, Python/PySpark, and AWS ecosystem (S3, EMR, Lambda, Redshift).
Experience with data acquisition/transformation tools (Fivetran, DBT), SQL performance tuning, and workflow orchestration tools (Apache Airflow or Astronomer).
Mid to senior-level data engineer experienced with scalable, cloud-native data architectures and operationalizing large data workloads in digital product contexts.
Strong technical leader able to document designs, troubleshoot data issues, implement automation, and improve data models for business intelligence.
Experienced in real-time data streaming, optimization of big data processes, and implementing security and privacy controls.