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Tier-1 brand, mid-level data role, metro location, and broad requirements drive high competition.
Core data engineering skills (Spark, Python, Kafka, cloud) are broadly transferable across industries.
Explicit 5-8 years plus extensive mandatory big-data, cloud, and language skills make shortlisting highly strict.
Design, develop, and implement data pipelines and data integration solutions leveraging big data and cloud technologies (AWS).
Utilize technologies including Spark, Kafka, Airflow, DBT, Flink, Apache Iceberg, and Datadog to build robust data infrastructure supporting analytics.
Implement CI/CD pipelines in conjunction with code repositories like GitHub to streamline data engineering workflows.
5-8 years of work experience in data engineering or related roles.
Bachelor's degree in Technology (B.Tech, M.Tech, M.E, MCA, or B.E).
Strong technical skills mandatory: Big Data, AWS, SQL, Python/Scala, Spark, S3, Glue, EMR, Aurora Postgres, Lambda, Kinesis, Kafka, Airflow, DBT, Flink, Apache Iceberg, Datadog.
Experience with cloud and big data tools such as AWS services; knowledge of Snowflake is a plus but not mandatory.
Experienced in building scalable data pipelines and infrastructure within cloud environments, specifically AWS.
Comfortable working with advanced big data and streaming technologies including Kafka, Flink, and Apache Iceberg.
Capable of implementing and maintaining CI/CD workflows for data engineering and integration projects.