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Mid-level generalist data/ML role in a metro with broad AWS skillset increases candidate competition.
Core data engineering skills on AWS transfer easily across industries.
Explicit 5+ years, mandatory AWS experience, and detailed tech-stack requirements make filtering strict.
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Design, build, and optimize large-scale ETL/ELT data pipelines and infrastructure on AWS to power analytics, ML models, and business reporting.
Orchestrate complex data workflows using Apache Airflow ensuring reliability, observability, and SLA adherence.
Manage data storage solutions (S3, Redshift, RDS), containerized applications (Docker, ECS), and implement infrastructure-as-code for scalable data delivery.
5+ years professional experience in data engineering, including at least 3 years with AWS production workloads.
Proficiency with AWS services: EMR (Spark), Apache Airflow, S3, Redshift, RDS, Lambda, and ECS.
Strong skills with Docker, Python, SQL, and experience using AI-assisted coding tools within IDEs.
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field; Location: Kolkata (Rajarhat–Newtown); Work from office.
Experienced in architecting scalable data pipelines and infrastructure using modern AWS services with a focus on cost optimization and data governance.
Operates effectively at the intersection of Data Engineering and ML Ops, collaborating with data scientists to productionize ML models and feature pipelines.
Demonstrates expertise in containerization, infrastructure as code, and leverages AI-assisted development tools to accelerate delivery and maintain code quality.