Principal DataOps Engineer

W2 ONLY
No C2C assistance - Corp agencies, please do not call.
6-months + project
While this is 100% remote, candidates should be willing to support stakeholders located in both the EST and PST time zones.
We are looking for candidates with 10+ years of experience supporting Data Platform Engineering and/or DataOps/DevOps.
Key Responsibilities:
Data Platform Engineer/DevOps Engineer

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
• 10+ years of experience in Data Engineering, Platform Engineering, DevOps, Site Reliability Engineering (SRE), or Cloud Engineering.
• Experience with:
  • Data ingestion
  • Data transformation
  • Data quality checks
  • Data lineage tracking
  • Data cataloging
  • Deployment of ETL/ELT pipelines
• Hands-on experience with cloud platforms such as:
  • Azure
  • AWS
  • Google Cloud Platform (GCP)
• Strong experience with:
  • Terraform
  • Git
  • CI/CD pipelines
  • Container technologies
  • Infrastructure automation
• Experience supporting enterprise-scale data platforms and cloud-native applications.
• Strong troubleshooting and production support experience.
• Understanding of data governance, security, and compliance requirements.
• Excellent communication and collaboration skills.

Position Summary
We are seeking a highly motivated Data Platform Engineer with deep hands-on expertise in cloud infrastructure, DataOps, DevOps, and modern data platform technologies. This role will be responsible for designing, building, automating, and supporting enterprise-scale data platforms that power analytics, AI, and business-critical workloads.
The ideal candidate combines strong technical engineering skills with practical experience in Infrastructure as Code, CI/CD automation, cloud-native architectures, data platform operations, observability, and governance. You will work closely with Data Engineering, Analytics, Architecture, Security, and Product teams to deliver secure, scalable, and highly reliable data solutions.
Key Responsibilities
Data Platform Engineering

• Design, develop, and maintain enterprise-scale cloud data platforms supporting analytics, reporting, machine learning, and AI workloads.
• Build and optimize data pipelines, orchestration frameworks, and platform automation capabilities.
• Develop scalable and reusable infrastructure components to accelerate platform deployment and management.
• Support modern data architecture patterns, including Data Mesh and data product enablement.

Infrastructure as Code & Automation

• Build and manage cloud infrastructure using Terraform and Infrastructure as Code (IaC) best practices.
• Automate provisioning, deployment, configuration, and monitoring processes.
• Develop reusable modules and templates to standardize infrastructure deployment across environments.
• Ensure infrastructure complies with organizational security and governance standards.

CI/CD & DevOps

• Design, implement, and maintain CI/CD pipelines using modern DevOps practices.
• Automate application, infrastructure, and data platform deployments.
• Integrate testing, validation, security scanning, and release management into deployment pipelines.
• Improve developer productivity through platform engineering and self-service capabilities.

Platform Reliability & Operations

• Monitor platform health, availability, and performance using observability and telemetry tools.
• Troubleshoot system incidents, root cause issues, and implement preventative measures.
• Participate in production support, incident response, and operational readiness activities.
• Improve platform resiliency, disaster recovery, and business continuity capabilities.

Data Governance & Security

• Implement data governance, metadata management, and data quality frameworks.
• Support data lineage, cataloging, compliance, and access control requirements.
• Work closely with security teams to enforce zero-trust architecture principles.
• Ensure compliance with enterprise security, privacy, and regulatory standards.

AI-Driven Engineering

• Leverage AI-powered development tools to improve engineering productivity.
• Implement intelligent automation for testing, deployment validation, and operational support.
• Evaluate and adopt emerging AI engineering practices and tools.
• Build automation workflows to reduce repetitive manual operational tasks.

Collaboration & Technical Leadership

• Partner with architects, data engineers, analytics teams, and cloud engineers to deliver platform capabilities.
• Participate in architecture reviews, technical design sessions, and platform governance forums.
• Contribute to platform strategy, roadmap planning, and technology evaluations.