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Known multinational, metro location, popular mid-level data role with broad skillset increases competition.
Data engineering skills transfer across industries but require specific cloud and big-data domain experience.
Explicit 3+ years plus mandatory AWS, big-data, and governance skills enforce strict screening.
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Design, build, and maintain scalable ETL/ELT data pipelines and platforms using AWS services (Glue, Lambda, Step Functions), Python, and big data tools.
Manage structured and unstructured data storage across AWS services (S3, Redshift, DynamoDB, RDS, etc.) with focus on performance tuning, data security, and governance compliance.
Collaborate in Agile teams with Data Scientists, Analysts, and BI to translate business needs into reliable, monitored, and secure data solutions, including CI/CD and DevOps practices.
3+ years of professional experience in data engineering or software engineering with strong data focus.
Hands-on expertise with AWS data services (S3, Glue, Redshift, Athena, Lambda, Kinesis, DynamoDB, Lake Formation) and big data technologies (Apache Spark, Databricks, Hadoop, Kafka).
Proficiency in Python, SQL, Scala, and experience with DevOps tools such as Git, CI/CD pipelines, CloudFormation, Terraform or CDK.
Bachelor’s Degree in Engineering/Computer Science or equivalent experience required.
Experienced in developing and operating complex, production-grade data pipelines and large-scale data lakes or warehouses within AWS environments.
Skilled in implementing strong data security, compliance standards (e.g., GDPR), and governance aligned with Secure SDLC and privacy policies.
Operates efficiently in Agile, cross-functional teams with ability to clarify ambiguous business requirements into scalable technical data solutions.