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Tier-1 brand, common Data Engineer title, and mid-level (2-5yr) experience attract high applicant competition.
Core data engineering skills (AWS, Glue, SQL, Python) are broadly transferable across industries.
Mandatory 2+ years plus extensive AWS, data platform, and language requirements enforce strict shortlisting.
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Design, develop, and maintain secure, stable, and scalable data collection, storage, access, and analytics platforms using AWS technologies and data pipeline tools.
Produce architecture and design documentation for complex applications and ensure code meets design constraints and security standards.
Leverage AI-assisted development tools to improve code quality and delivery speed while validating AI-generated outputs through peer review and automated testing.
2+ years of applied software engineering experience with formal training or certification.
Proficiency in Java and Python programming languages and SQL-based technologies (e.g., MySQL, Oracle).
Hands-on experience with AWS cloud services including S3, IAM, KMS, Secrets Manager, Lake Formation, Glue, Athena, EMR, Kinesis or MSK, and orchestration tools like Airflow or serverless workflows.
Experience using enterprise-authorized AI-assisted software development tools and strong understanding of responsible AI use including data sensitivity and security.
Experienced in building data lakes, data platforms, and data pipelines on AWS using Lake Formation, Glue, and related services.
Able to produce and maintain high-quality, secure, production-ready code in a complex, large corporate environment.
Skilled in architecting data solutions with strong knowledge of AWS security controls and modern data serialization formats like Parquet, Iceberg, Avro, or JSON-LD.