Englewood, CO30+ days ago
Skills and Qualifications: • Advanced proficiency in Shell scripting, Python, and SQL, with demonstrated experience developing, optimizing, and maintaining scalable, production-grade data pipelines • Strong hands-on experience with AWS data services (EC2, S3, Redshift) and solid understanding of core cloud architecture components including VPC, IAM, CloudWatch, and Data Lake frameworks • Experience designing and managing workflow orchestration using tools such as Control-M or Apache Airflow to support reliable, automated data processing • Deep understanding of data modeling concepts, including dimensional and 3NF structures, ensuring high-quality and performant data solutions • Proficiency with Git/GitLab for version control, peer code reviews, and CI/CD processes, along with working knowledge of Infrastructure-as-Code tools such as Terraform or AWS CloudFormation • Proven ability to evaluate and communicate findings from Proof-of-Concept (POC) initiatives, with strong analytical and problem-solving skills; familiarity with Generative AI tools (e.g., Amazon Q, Gemini, Databricks Genie Rooms) and their practical application within data engineering workflows. Key Responsibilities: • Monitor and provide operational support for large enterprise data warehouse systems, resolving complex ETL job-related issues and ensuring data quality • Maintain and optimize scalable batch and streaming data pipelines and data lake solutions on the AWS cloud platform (S3, Glue, EC2, Redshift) • Lead incident management and root cause analysis meetings to develop operational metrics and drive continuous improvement of production systems • Collaborate with cross-functional Agile/Scrum teams (Data Scientists, Analysts, DevOps) to implement data transformation logic (ETL/ELT) • Manage CI/CD pipelines and Infrastructure-as-Code (IaC) using GitLab while exploring Generative AI integration points like Amazon Q for pipeline optimization • Participate in shift-based working hours and on-call support to ensure the continuous reliability and performance of enterprise data systems.