Job Description

Job Requirements

Demonstrate deep technical understanding of product design/ Architecture with hands-on development capability Initiate/Incubate/execute PoCs to validate the use cases targeting enhanced ETL/DWH engineering solutions. Provide Architectural Guidance the team about data pipelines/ storage/ operations/ client delivery. Initiate and drive cost optimization programs leverage design & Infra enhancements. Implement best practices for data engineering, ETL, and DWH development in AWS cloud environment Conduct code reviews; Coach team members on coding practices and drive adherence to coding standards. Collaborate with cross-functional teams to finalize project requirements, timelines, and deliverables. Monitor project progress, identify risks, and ensure on-time delivery of data engineering projects. Optimize data models for performance and scalability; leverage latest AWS services to supplement scalable and configurable DWH architectures Ensure project implementations/ solutions in compliant to security and privacy standards per HIPAA guidelines. Stay abreast with the latest trends in data engineering and AWS technologies; Continuously evaluate tools and services to improve data engineering outcomes. Proactively identify & resolve issues relating to people, product, tech, and processes.

Work Experience

Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field. 5+ yrs. experience in data engineering roles. 5+ years of experience in DWH/ETL Domain using AWS tech stack such as Redshift, S3, Glue, Lambda and IAM. Proficiency in PySpark/Python/SQL and designing data models and data standards. Deep understanding of ETL/ELT processes, data warehousing concepts, and big data technologies. Experience building & managing data pipelines using tools like Glue, Airflow, AWS Step Functions, or similar. Experience in RDMS & No-SQL databases: SQL Server databases, PostgreSQL, DynamoDB, or Aurora. Experience in dimensional modeling & schema design; Star and Snowflake model Proven ability to lead & mentor data engineers towards innovative and disciplined environment. Strong problem-solving skills, with a process/methodical approach to resolve technical challenges and risks * Excellent analytical, verbal and communication skills for stakeholder and team management.