DataOps & MLOps Lead — Databricks Lakehouse

Protective; Birmingham (AL); On-site; USD 140,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Mental health benefits

Employee assistance program

Paid time off

Parental leave

Short-term disability

Cultural observance day

401(k) with company matching

ProHealth Rewards

Job summary

Protective Life is transforming how it builds and operates software, moving to a product operating model with empowered, outcome-oriented teams. The DataOps/MLOps Lead will own the platform, set standards, and automate the backbone of data and ML pipelines on the Databricks Lakehouse on Azure.

You will mentor engineers, drive reliability, security, and cost governance in a regulated insurance environment, and partner with the Voyager product pod across Life, Annuities, and Employee Benefits

Qualifications

• 8+ years in data, ML, or platform engineering, or in SRE/DevOps, including several years operating production data and/or ML systems.
• Demonstrated technical leadership — setting standards, building paved paths and automation, and mentoring engineers (formal people management not required, but valued).
• Strong CI/CD expertise with Azure DevOps (ADO) — build/release pipelines, environment promotion, automated testing — and Git-based workflows.
• Hands‑on experience with orchestration (Dagster or equivalent) and the modern data stack — dlt (dltHub) ingestion and dbt modeling — on a Databricks lakehouse (Delta Lake).
• MLOps experience — MLflow model registry, model deployment/serving, monitoring, drift detection, and retraining automation.
• Infrastructure‑as‑code and cloud platform administration on Microsoft Azure (compute, storage, identity, networking basics); Terraform or equivalent.
• Strong Python and SQL for automation and tooling.
• Experience with observability/monitoring tooling and SRE practices — SLAs/SLOs, alerting, and incident management.
• Demonstrated rigor in security, access control, and secure, compliant handling of sensitive data.
• Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience.

Responsibilities

• Own the DataOps/MLOps platform and operating model — the paved paths, automation, and tooling that data and ML engineers use to build and run pipelines and models reliably.
• Lead CI/CD standards and pipelines in Azure DevOps (ADO) for data pipelines and ML models — build, test, and release automation, environment promotion, and repeatable, auditable deployments.
• Standardize orchestration on Dagster — reusable assets, scheduling, backfills, dependency management, and run observability across the pod's pipelines.
• Operationalize the ingestion and transformation stack — dlt (dltHub) and dbt — with automated testing, CI checks, and safe deployment of changes.
• Build MLOps foundations with the ML engineering team — MLflow model registry, Databricks Model Serving, automated deployment, monitoring, drift detection, and retraining triggers.
• Establish data and model observability — freshness, quality, lineage, latency, drift, and cost — with alerting and clear SLAs/SLOs.
• Administer and govern the Databricks Lakehouse on Azure — workspace configuration, Unity Catalog governance, access controls, and policy automation.
• Manage infrastructure as code and environments — reproducible dev/test/prod setups (e.g., Terraform), secrets management, and least-privilege access.
• Own reliability and incident practices — on-call, runbooks, root-cause analysis, and continuous improvement for data and ML services.
• Drive cost visibility and optimization (FinOps) across compute, storage, and model serving.
• Automate governance and compliance controls — audit logging, model and pipeline inventories, approval workflows, and evidence collection for a regulated environment.
• Provide technical leadership and mentoring — coach engineers on operational excellence and set the platform standards the pod builds on.

Skills

CI/CD

Azure DevOps

Dagster

Python

SQL

Observability

Security

Leadership

Education

BS Computer Science

Tools

Databricks

Terraform

Azure

Unity Catalog

MLflow

dbt

Docker

Kubernetes

Job description

Protective Life is transforming how it builds and operates software, moving to a product operating model with empowered, outcome-oriented teams. The DataOps/MLOps Lead will own the platform, set standards, and automate the backbone of data and ML pipelines on the Databricks Lakehouse on Azure.

You will mentor engineers, drive reliability, security, and cost governance in a regulated insurance environment, and partner with the Voyager product pod across Life, Annuities, and Employee Benefits