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Tier-1 brand, mid-level generalist data role, metro location, and broad skillset create high competition.
Core AWS and data engineering skills (ETL, Spark, SQL) are highly transferable across industries.
Explicit 3+ years and many mandatory AWS/big-data tech requirements make shortlisting highly strict.
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Design, build, and maintain scalable ETL/ELT data pipelines using AWS Glue, Lambda, Step Functions, and big data tools.
Manage structured and unstructured data storage using AWS services like S3, Redshift, DynamoDB, and optimize performance of data workflows.
Implement data security and governance using encryption, IAM roles, Lake Formation, and monitor pipeline health via CloudWatch and CloudTrail.
3+ years professional experience in data engineering or software engineering with strong data focus.
Hands-on experience with AWS services including S3, Glue, Redshift, Athena, EMR, Lambda, CloudFormation, Kinesis, DynamoDB, SQS, SNS, Lake Formation.
Proficiency in Python, SQL, Scala and big data tools such as Apache Spark, Databricks, Hadoop, Kafka.
Bachelor’s degree in Engineering/Computer Science or equivalent experience.
Experienced in delivering complex, production-grade data pipelines and platforms across multiple components.
Skilled at translating ambiguous business requirements into scalable and governed data solutions within Agile cross-functional teams.
Demonstrates strong data security and governance mindset aligned with Secure SDLC and data protection practices.