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Tier-1 brand, metro locations, broad skillset, and a popular mid-level data role increase competition.
Core data engineering skills are broadly transferable across industries, so background sensitivity is low.
Mandatory six years PySpark/data engineering experience and specific AWS/ETL/LLM skills make filters strict.
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Engineer, maintain, and optimize high-performance, secure, and robust PySpark-based software solutions within a feature team responsible for full software lifecycle.
Collaborate with engineers, architects, and business analysts to deliver complex and critical software rapidly with business value.
Design, test, implement, and support software solutions leveraging AWS services, focusing on data engineering and ETL processes.
At least 6 years of experience in PySpark and data engineering with strong ETL design, data quality testing, cleansing, monitoring, sourcing, and exploration expertise.
Proficient in Python, Spark, SQL, and experience with AWS Glue, Lambda, EMR/Spark, and S3.
Demonstrated knowledge of Large Language Models (LLMs) and AI-assisted development tools including practical prompt engineering skills.
Experience working with development/testing tools, bug tracking, multiple programming languages or low code tools, plus DevOps and Agile methodologies.
Experienced in solving highly complex analytical and numerical problems within software engineering and data pipeline contexts.
Comfortable working end-to-end across software development lifecycle including coding, testing, deployment, and operations in an Agile environment.
Able to implement programming best practices focusing on scalability, automation, optimization, availability, and performance.