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Tier-1 brand, mid-level generalist data role, metro location, and broad required tech stack increase competition.
Data engineering skills are broadly transferable across industries, lowering background sensitivity.
Explicit 3+ years plus mandatory Spark, Python, AWS and security requirements indicate strict filters.
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Develop, test, and maintain critical data pipelines and architectures supporting multiple business functions using Spark, Python, and SQL.
Design and deliver scalable, secure data collection, storage, access, and analytics solutions for enterprise data protection and business objectives.
Leverage enterprise-authorized AI-assisted tools to improve code quality, delivery speed, and system architecture while ensuring secure coding standards.
3+ years of applied experience with formal training or certification in software engineering concepts.
Strong hands-on experience with Big Data technologies including Spark (Spark SQL, Spark Streaming) and Python (Pandas).
Proficiency with relational (RDBMS) and NoSQL databases, Linux/UNIX environments, and performance tuning for Python and Spark.
Experience with AWS cloud services including Lake Formation, Glue ETL or EMR, S3, Glue Catalog, Athena, Kinesis or MSK, Airflow or Lambda + Step Functions + Event Bridge; knowledge of AWS security services (IAM, KMS, Secrets Manager).
Strong expertise in end-to-end data lifecycle management and spark-based ETL/ELT/reporting frameworks within enterprise environments.
Experience incorporating AI-assisted software development tools with the ability to critically validate AI outputs for code quality and security.
Familiarity with SDLC, CI/CD, agile methodologies, and understanding of secure engineering workflows, including data sensitivity and responsible AI use.