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Tier-1 brand, popular Data Engineer title, mid-level experience, and metro location drive high competition.
Core data engineering skills (Python, SQL, Spark, AWS) are highly transferable across industries.
Explicit 3+ years plus mandatory cloud, Spark, Python, SQL, and CI/CD expectations increase shortlisting strictness.
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Design, build, and maintain scalable data products and infrastructure on AWS, including data ingestion strategies, formats, and metadata integrations.
Develop and operate robust data pipelines and CI/CD pipelines supporting AI/ML workflows with collaboration across Data Scientists, ML Engineers, and Product teams.
Enable advanced analytics and AI/ML use cases by designing efficient data-access patterns and tooling, with active participation in design discussions and cross-team collaboration.
Bachelor's or Master's degree in Computer Science, Engineering, or related discipline.
3+ years of hands-on experience working with data at scale.
Proficiency in Python and SQL with strong understanding of SQL and NoSQL databases and performance optimization in distributed systems.
Experience building and maintaining data and CI/CD pipelines on AWS platforms.
Experienced in Apache Spark (preferably PySpark) and practical AWS-native services like Lambda, Glue, SageMaker, S3, and Infrastructure as Code tools (CDK, CloudFormation, Terraform).
Strong collaboration experience with Data Scientists and Machine Learning Engineers, preferably with exposure to MLOps and AI product lifecycle.
Interest or domain experience in IoT, time-series data, automation systems, digital twins, or smart building technologies.