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Tier-1 brand, metro location, mid-level band, and broad popular tech stack increase competition.
Data engineering skills and tooling are broadly transferable across industries, so sensitivity is low.
Explicit 5–8 years requirement plus many mandatory technologies increases screening strictness.
Design, develop, and implement robust data pipelines and data integration solutions using big data and cloud technologies.
Leverage skills in SQL, Spark, Python/Scala, and tools like Kafka, Airflow, DBT to enable efficient data processing and analytics.
Contribute to building data infrastructure and systems, including CI/CD framework implementation with code repositories (e.g., GitHub).
5-8 years of relevant experience in data engineering and analytics roles.
Bachelor's degree in Technology (B.Tech / M.Tech / M.E / MCA / B.E).
Proficiency with big data technologies, AWS cloud services (including S3, Glue, EMR, Aurora PostGres, Lambda, Kinesis), SQL, Spark, Python or Scala.
Experience with Kafka, Airflow, DBT, Flink, Apache Iceberg, Datadog and familiarity with CI/CD frameworks; Snowflake and Microsoft Azure knowledge preferred.
Experienced in designing scalable data solutions in cloud environments, primarily AWS with Azure knowledge as a plus.
Comfortable working with streaming data and orchestration tools like Kafka and Airflow, enabling real-time and batch data processing.
Skilled at integrating multiple data engineering tools and frameworks, capable of implementing CI/CD in data pipelines for automation and reliability.