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Tier-1 brand, mid-level experience band, metro location, and broad required skills increase competition.
Core data engineering skills transfer across industries, though finance domain experience is preferred.
Explicit 2+ years plus mandatory AWS, Java/Python, Lake Formation and serialization skills create high filter strictness.
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Design, enhance, and deliver secure, stable, scalable data collection, storage, access, and analytics solutions within an agile engineering team.
Develop, troubleshoot, and maintain production-quality code and algorithms, applying software development life cycle tools and AI-assisted development capabilities.
Build and operate data lakes and pipelines on AWS using tools like Lake Formation, S3, Glue, Athena, streaming (Kinesis/MSK), and orchestration (Airflow/Lambda).
2+ years of applied experience in software engineering with formal training or certification.
Proficiency in Java and Python programming languages, SQL-based databases (MySQL/Oracle), and AWS cloud technologies including S3.
Hands-on experience with enterprise-authorized AI-assisted software development tools and ability to critically evaluate AI outputs.
Experience building data lakes and data pipelines on AWS using Lake Formation, Glue Catalog, Athena, Glue ETL/EMR, Kinesis/MSK, and orchestration tools like Airflow or Lambda workflows.
Experienced in developing data platform solutions with secure and scalable architecture focus in large corporate environments.
Proficient in AI-assisted development workflows with understanding of responsible AI use, security, and data sensitivity considerations.
Familiar with AWS security controls (IAM, KMS, Secrets Manager, Lake Formation governance) and modern data serialization formats (Parquet, Iceberg, Avro, JSON-LD).