DataOps & MLOps Lead: Build Reliable AI Pipelines

Protective; Nebraska; Hybrid; USD 125,000 - 170,000; Full time

Benefits offered by this job

Health insurance

Dental & vision coverage

401(k) with company match

Paid time off

Parental leave

Mental health benefits

Job summary

Protective Life seeks a DataOps/MLOps Lead to own the platform and operating model for data and ML pipelines on a Databricks Lakehouse in Azure. You’ll set standards, automate the backbone, mentor engineers, and ensure reliability, security, cost, and auditability in a regulated insurance environment.

You will lead CI/CD, Dagster orchestration, data ingestion with dltHub/dbt, and MLOps foundations including MLflow and model serving, while driving observability and governance across the pod.

Qualifications

• 8+ years in data, ML, or platform engineering and production systems.
• Demonstrated technical leadership and mentoring.
• Strong Azure DevOps, CI/CD, and Git workflows.
• Hands-on Dagster/dltHub/dbt on Databricks Lakehouse.
• MLOps: MLflow, model registry, deployment and monitoring.

Responsibilities

• Own DataOps/MLOps platform and operating model for pipelines.
• Lead CI/CD standards and Azure DevOps for data workloads.
• Standardize Dagster orchestration and observability across pipelines.
• Operationalize ingestion/transform stack with automated testing.
• Build MLOps foundations: model registry, serving, retraining.
• Establish data/model observability with SLAs/SLOs.
• Govern Databricks Lakehouse on Azure and access controls.
• Manage IaC and environments with Terraform and secure secrets.
• Own incident practices and continuous improvement for data services.
• Drive FinOps cost visibility across compute and storage.

Skills

Python

SQL

CI/CD

SRE

Data pipelines

Education

Bachelor's degree in CS/Engineering or related

Tools

Azure DevOps

Dagster

dbt

Databricks

Terraform

Kubernetes

Azure

MLOps tooling

Job description

Protective Life seeks a DataOps/MLOps Lead to own the platform and operating model for data and ML pipelines on a Databricks Lakehouse in Azure. You’ll set standards, automate the backbone, mentor engineers, and ensure reliability, security, cost, and auditability in a regulated insurance environment.

You will lead CI/CD, Dagster orchestration, data ingestion with dltHub/dbt, and MLOps foundations including MLflow and model serving, while driving observability and governance across the pod.