Devops Engineer-BI /Data Science

NAVA Software solutions is looking for a Devops Engineer BI /Data Science

Details:

Analytics DevOps Engineer

Location: Spring TX - Hybrid 3 days a week

Duration: 6-12 months

The BI/Data Science DevOps Engineer will be responsible for building and maintaining the CI/CD pipelines, deployment automation, and platform infrastructure that support the organization's Power BI, data science, and analytics ecosystem. This role bridges traditional DevOps practices with the specific needs of BI and ML environments - managing release processes for Power BI content, Databricks workspaces, and data science model deployments. The BI/Data Science DevOps Engineer takes complete ownership of deployment reliability, environment consistency, and operational governance across the analytics platform.

Job Duties/Roles

• Design, build, and maintain CI/CD pipelines for Power BI reports/datasets, Databricks notebooks/jobs, and ML model deployments across dev, test, and production environments.
• Automate deployment of Power BI workspaces, datasets, and dataflows using Power BI REST APIs, deployment pipelines, and source-controlled artifacts.
• Manage Databricks infrastructure-as-code (clusters, jobs, workflows, Unity Catalog objects) using tools such as Terraform, Databricks Asset Bundles, or equivalent.
• Establish and enforce version control practices (Git-based workflows) for BI artifacts, notebooks, and ML code across teams.
• Build monitoring, alerting, and logging for pipeline health, job failures, deployment errors, and platform performance across the BI/data science stack.
• Standardize environment promotion processes (dev test prod) with appropriate approval gates, testing, and rollback procedures.
• Collaborate with Data Science and BI teams to containerize and package models/reports for repeatable, automated deployment.
• Act as the liaison between Data Science, BI, Data Engineering, and Infrastructure/IT teams to align deployment practices with enterprise standards.
• Manage access provisioning, secrets, and credentials across environments using secure vaults and role-based access controls.
• Support cost governance and resource optimization by tracking compute usage, autoscaling policies, and cluster configurations.
• Troubleshoot deployment failures, environment drift, and integration issues across the analytics and data science toolchain.
• Document deployment architecture, runbooks, and standard operating procedures for platform reliability and knowledge continuity.

Knowledge, Skills, and Abilities Required (KSAR)

• Highly proficient in CI/CD tooling such as Azure DevOps, GitHub Actions, or Jenkins.
• Highly proficient in scripting and automation (PowerShell, Python, Bash) for deployment workflows.
• Strong working knowledge of the Power BI platform, including deployment pipelines, REST APIs, and workspace/app management.
• Strong experience with Databricks, including Databricks Asset Bundles, workflows, cluster policies, and Unity Catalog administration.
• Experience with infrastructure-as-code tools such as Terraform or ARM/Bicep templates.
• Proficient in Azure suite services relevant to data platforms - Azure Data Factory, Azure Data Lake, Azure Key Vault, Azure Synapse.
• Solid understanding of Git-based version control and branching strategies for analytics and ML artifacts.
• Familiarity with containerization and orchestration concepts (Docker, Kubernetes) as applied to model/report deployment.
• Understanding of MLOps concepts - model versioning, monitoring, and automated retraining/deployment pipelines - is a strong plus.
• Excellent verbal, written, and presentation skills, able to translate technical deployment concepts to non-technical stakeholders.
• Attentive to detail, with strong troubleshooting and root-cause analysis skills.
• Ability to manage highly confidential material and enforce security/compliance standards.

Minimum Years of Experience

Typically, 5 or more years of experience in DevOps, platform engineering, or infrastructure roles, with at least 2 years focused on BI or data science/ML deployment environments.