DevOps Engineer II - Data and Analytics

Expand Energy•Oklahoma City, OK

13d

About The Position

This position is a technical role responsible for ensuring the reliability, scalability, and operational excellence of enterprise data and analytics platforms and related technologies. This role partners closely with developers, data engineers, and platform owners to design resilient systems, improve deployment practices, and automate operations. This position contributes to DevOps and cloud engineering practices by supporting automation efforts, enhancing system observability, and assisting with incident response and continuous improvement initiatives. This role combines hands-on technical skills with a focus on system performance, reliability, and operational efficiency across a diverse enterprise platform landscape.

Requirements

Experience deploying and supporting infrastructure using infrastructure-as-code tools such as Terraform, ARM, Bicep, or similar technologies
Experience supporting and administering enterprise SaaS and PaaS platforms, including configuration, environment management, and operational support
Strong foundational knowledge of operating systems, including Linux and Windows Server
Understanding of distributed systems, microservices architectures, data pipelines, and resiliency patterns
Working knowledge of identity and access management concepts, including integration with identity providers (e.g., Azure AD/Entra ID), single sign-on (SSO), and role-based access controls
Familiarity with modern authentication and authorization patterns, including OAuth 2.0, OpenID Connect (OIDC), and token-based access for APIs and services
Familiarity with networking and security concepts for cloud and SaaS platforms, including VNET/VPC configuration, private connectivity, and secure service access
Experience managing secrets and secure configuration using tools such as Azure Key Vault or similar vault technologies
Experience building and supporting CI/CD pipelines using tools such as Azure DevOps, GitHub, or similar platforms
Understanding of DevOps practices, including deployment automation, environment management, release strategies, and change control
Proficiency in scripting and automation using tools such as Python, PowerShell, Bash, or similar scripting languages
Experience automating platform operations through APIs, SDKs, command-line tools, or workflow automation
Familiarity with automating routine data platform operations such as access provisioning, job retries, health checks, usage reporting, and environment configuration
Experience with monitoring and observability tools such as Splunk, Azure Monitor, Datadog, Grafana, Prometheus, or similar platforms for log aggregation, alerting, and operational insights
Understanding of incident management, problem management, and reliability practices, including ITIL-based or similar operational processes
Familiarity with performance tuning, system optimization, and capacity planning concepts
Understanding of data pipeline reliability concepts, including job monitoring, failure alerting, dependency tracking, retry patterns, and service-level expectations
Awareness of AIOps concepts, intelligent alerting, and automation in support of system and platform reliability
Experience with enterprise data and analytics platforms such as Snowflake, Databricks, dbt, Sigma, or similar systems, with the ability to support environments and troubleshoot operational issues
Working knowledge of Snowflake administration concepts, including warehouse management, RBAC, query performance, resource monitoring, cost governance, and secure data sharing
Working knowledge of BI and analytics platforms such as Sigma, Power BI, Tableau, or similar tools, including workspace management, connection monitoring, and performance troubleshooting
Working knowledge of data orchestration and pipeline tools such as Azure Data Factory, dbt Cloud, Airflow, Prefect, or similar platforms
Understanding of lakehouse and cloud data storage patterns, including Delta Lake, ADLS, S3, or similar technologies
Knowledge of AI/ML and data science platforms, including environments used for model development, deployment, monitoring, access management, and operational support
Minimum: 2 years related work experience

Nice To Haves

Preferred: Bachelor’s degree - from accredited university - IT, MIS, Computer Science or related field

Responsibilities

Partner with data engineering, application, analytics, and platform teams to design and support reliable, scalable, and secure systems, including data warehouse, lakehouse, analytics, and data integration platforms
Develop strong working knowledge of supported data platforms and services, including Snowflake, Databricks, dbt, Sigma, Azure, and related cloud services, to identify reliability risks, performance issues, and cost optimization opportunities
Support administration and configuration of Snowflake environments, including warehouse management, access controls, performance optimization, resource monitoring, and cost governance
Support analytics platform operations for tools such as Sigma or similar BI/analytics platforms, including workspace configuration, connection health, availability, and performance troubleshooting
Design and implement infrastructure and platform automation using infrastructure-as-code, configuration management, and scripting practices
Participate in incident response efforts, including troubleshooting, root cause analysis, and post-incident reviews across data platforms, pipelines, and related services
Develop and maintain observability solutions for data platforms and pipelines, including monitoring, logging, alerting, data pipeline health checks, and operational dashboards
Identify and eliminate manual operational processes through scripting, automation, self-service capabilities, and platform engineering solutions
Collaborate with data engineering and analytics teams to improve performance, fault tolerance, resiliency, and operational reliability across data pipelines and analytics workloads
Contribute to capacity planning, cost optimization, and scalability initiatives, including Snowflake credit usage, Databricks compute utilization, and cloud resource consumption
Assist with the setup, administration, and operational support of AI/ML and data science platforms, including environments for model development, deployment, monitoring, and secure access
Evaluate and recommend tools, technologies, and approaches to improve data platform reliability, engineering productivity, and analyst experience
Document architecture, operational processes, runbooks, automation patterns, and reliability standards