Lead DataOps / MLOps at Protective

Department: Information Technology (IT)

Compensation

$124,500 – $170,000/yr

Setup: Remote
Location: Birmingham, Alabama
Type: Full-time
Level: lead/mgmt
Posted: Sep 15, 2026

Description

Protective Life is hiring a DataOps/MLOps Lead to own the platform and operating model for data and machine learning engineers. The role builds and governs CI/CD, orchestration, observability, environments, security, cost, and compliance automation on Databricks Lakehouse and Microsoft Azure, while mentoring engineers and ensuring reliable, auditable production systems. It requires 8+ years of relevant experience, strong Azure DevOps, Dagster, modern data-stack, MLOps, infrastructure-as-code, Python, SQL, and observability expertise, and offers a targeted salary of $124,500 to $170,000 plus incentive opportunities.

Required Skills

CI/CD
Azure DevOps
Dagster
dlt
dbt
MLflow
Databricks
Terraform
Python
SQL
Observability
SRE

Additional Information

Minimum Education: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Required Previous Experiences: 8+ years in data, machine learning, platform engineering, SRE, or DevOps, including several years operating production data or machine learning systems
CI/CD expertise with Azure DevOps, build/release pipelines, environment promotion, automated testing, and Git workflows
Hands-on experience with orchestration, the modern data stack, dlt ingestion, dbt modeling, and Databricks Lakehouse
MLOps experience with MLflow, model deployment and serving, monitoring, drift detection, and retraining automation
Infrastructure-as-code and Microsoft Azure cloud administration, including Terraform or equivalent
Python and SQL for automation and tooling
Observability, monitoring, SRE practices, SLAs/SLOs, alerting, and incident management
Security, access control, and secure handling of sensitive data
Preferred Qualifications: Financial services or insurance platform experience and familiarity with model risk and regulatory audit expectations
Databricks administration, Unity Catalog, cluster policies, Mosaic AI, and Azure Machine Learning
Containerization and orchestration with Docker, Kubernetes, Azure AKS, or Container Apps
Data-quality and observability tooling such as Great Expectations or Monte Carlo
Responsible-AI and model-governance automation
Relevant certification such as Databricks, Azure DevOps, or Azure Administrator
Translation Company Name: Protective