DataOps Engineer Pennsylvania, Erie 09/14/2026 Contract Active

Job Summary

We are seeking a DataOps Engineer with strong hands-on expertise in AWS, modern DataOps practices, data pipeline automation, observability, infrastructure as code, and cloud cost optimization. The role will focus on operating, automating, monitoring, and continuously improving core data platforms and structured data environments to maximize reliability, uptime, and efficiency. The ideal candidate will design robust ETL/ELT pipelines, establish strong operational standards, lead incident and release management, develop reusable data engineering frameworks, and leverage AI-assisted coding tools to improve engineering productivity. Key Responsibilities

• Operate, automate, monitor, and continuously improve core data platforms to improve data flow, reduce waste, and maximize uptime for structured datasets and analytics.
  
• Design and optimize robust data pipeline automation for ETL/ELT workloads, including orchestration, scheduling, and CI/CD.
  
• Implement deep observability, manage logs, automate health checks, and track SLAs/SLOs to improve data reliability.
  
• Lead rapid incident response, stakeholder communications, and root cause analysis (RCA) activities.
  
• Identify process gaps and implement corrective actions to prevent recurring incidents.
  
• Manage the end-to-end release lifecycle, including automated unit, performance, and end-to-end testing.
  
• Execute smooth deployments, rollbacks, and performance tuning activities.
  
• Partner with managed service providers to evaluate and adopt solutions aligned with DataOps best practices and contractual SLAs.
  
• Translate technical metrics into clear executive reports and collaborate with data and BI teams to improve the quality of data products.
  
• Design data engineering assets and develop reusable frameworks, patterns, and engineering standards.
  
• Leverage AI-assisted coding tools such as Codex to improve development efficiency and automation.
  
• Apply strong FinOps practices to monitor, control, and optimize cloud costs across large-scale data environments. Required Qualifications
  
• Strong hands-on experience with AWS and modern DataOps practices.
  
• Deep expertise in AWS infrastructure services, including EC2, EKS, S3, Glue, and Lambda.
  
• Strong knowledge of AWS IAM and implementation of stringent security standards across data pipelines.
  
• Strong experience with Medallion architecture.
  
• Proficiency in Infrastructure as Code using Terraform and AWS CloudFormation.
  
• Highly proficient in Python, PySpark, and SQL.
  
• Strong understanding of ETL/ELT pipeline design, orchestration, scheduling, automation, and CI/CD.
  
• Deep understanding of complex XML handling.
  
• Experience implementing observability, logging, automated health checks, and SLA/SLO monitoring for data platforms.
  
• Experience with incident response, root cause analysis, release management, automated testing, deployments, rollbacks, and performance tuning.
  
• Experience developing reusable data engineering frameworks and enforcing engineering standards.
  
• Strong understanding of cloud security practices across data engineering environments.
  
• Strong FinOps awareness with the ability to monitor and optimize cloud costs.