Data Engineer OVERVIEW OF ROLE • Design, develop, and maintain scalable, high-performance data engineering solutions using AWS, Databricks, Spark, and PySpark, leveraging strong technical acumen to solve complex data and engineering challenges. • Build robust data pipelines following Medallion Architecture (Raw, Silver, and Gold layers), covering data ingestion, transformation, processing, data modeling, validation, quality checks, and reconciliation. • Develop and optimize data solutions using AWS services including S3, Glue, Aurora/RDS, Lambda, and Step Functions, along with Databricks, Spark, and PySpark to improve performance, scalability, reliability, and cloud cost efficiency. • Implement reliable orchestration and operational workflows covering scheduling, dependencies, retries, error handling, monitoring, and failure recovery, while troubleshooting complex data and production issues and driving root-cause resolution. • Develop reusable, maintainable, and production-ready code following engineering standards and best practices; contribute to code reviews, testing, CI/CD, deployment, automation, and continuous improvement. • Support data migration and modernization initiatives across AWS and Databricks, including legacy platform migrations, source-to-target mapping, data validation, reconciliation, and production readiness. • Collaborate with Product, Business, Architecture, QA, API, and Engineering teams in a cross-functional Agile environment, contributing to technical design discussions, estimation, sprint planning, backlog refinement, and delivery. • Provide technical guidance and mentorship to other engineers as applicable, promote reusable frameworks and engineering standards, and contribute to resolving complex technical challenges. REQUIRED QUALIFICATIONS • 5+ years of experience in Data Engineering, ETL/ELT, and developing large-scale data pipelines, with experience providing technical guidance or leadership as applicable. • Strong hands-on experience with AWS services including S3, Glue, Aurora/RDS, Lambda, and Step Functions, with a strong understanding of cloud-native data engineering practices. • 2+ years of hands-on experience with Databricks, Spark, and PySpark, including Spark performance optimization, partitioning, joins, caching, file formats, data skew, and efficient job design. • Strong SQL and Python/PySpark skills, with experience in data modeling, database design, schema mapping, ETL/ELT, and source-to-target transformations. • Experience designing and optimizing data pipelines and orchestration workflows, including scheduling, dependencies, retries, error handling, monitoring, failure recovery, and performance optimization. • Strong understanding of software engineering practices including coding standards, code reviews, testing, Git, CI/CD, Azure DevOps, reusable frameworks, and automation. • Experience with data quality, validation, reconciliation, monitoring, troubleshooting, and root-cause analysis. • Understanding of REST APIs, API request/response flows, and integration with APIs and third-party source systems. • Experience creating, reviewing, and maintaining functional and technical documentation throughout the delivery lifecycle. • Strong communication and collaboration skills, with experience working across Product, Business, Architecture, QA, API, and Engineering teams in Agile environments. • Strong understanding of Agile practices, including sprint planning, backlog refinement, estimation, iterative delivery, and production support. 5 PREFERRED QUALIFICATIONS • Experience with API development and integration, including hands-on experience working with API-driven data solutions. • Experience with Redis or other in-memory databases and caching technologies. • Experience with Databricks and advanced Spark performance optimization techniques. • Experience developing reusable frameworks, automation, and engineering standards. • Relevant AWS certifications in Cloud, Data Engineering, or Solutions Architecture. • Experience developing, debugging, and supporting data solutions within large, cross-functional engineering teams. • Strong analytical and problem-solving skills, with the ability to manage multiple priorities in a deadlinedriven environment. • Strong attention to detail and commitment to data quality, reliability, and engineering excellence