Back
Tier-1 brand, popular mid-level data role, metro location, broad skill requirements increase applicant competition.
Role requires specialized big-data and cloud engineering skills, somewhat transferable but domain-specific expertise important.
Many mandatory big-data tools listed and explicit 5-8 years requirement, making filters highly selective.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and develop data infrastructure and pipelines using big data technologies to enable efficient data processing and analysis.
Implement and maintain data integration and transformation solutions leveraging cloud services like AWS and tools including Spark, Kafka, Airflow, and DBT.
Contribute to continuous integration/continuous deployment (CI/CD) practices with popular code repositories such as GitHub to support scalable data solutions.
5-8 years of professional experience in big data engineering or related fields.
Mandatory skills: Big Data, AWS, SQL, Python and/or Scala, Spark, S3, Glue, EMR, Aurora Postgres, Lambda, Kinesis, Kafka, Airflow, DBT, Flink, Apache Iceberg, Datadog.
Educational requirements: Bachelor’s degree in Engineering or related fields (B.Tech, M.Tech, M.E, MCA, B.E).
Experience with CI/CD frameworks and code repositories (e.g., GitHub) is required.
Proven expertise in designing and operating scalable big data architectures on AWS cloud environments.
Hands-on experience in multiple programming languages, preferably both Python and Scala, with Spark and streaming technologies like Kafka and Flink.
Experienced in modern data engineering tools and frameworks that support automation and orchestration such as Airflow, DBT, and CI/CD pipelines.