Lead DataOps / MLOps

About the role

The work we do has an impact on millions of lives, and you can be a part of it.

We help protect our customers against life’s uncertainties. Regardless of where you work within the company, you’ll be helping provide protection and peace of mind when our customers need it most.

Protective Life is transforming how it builds and operates software — moving to a product operating model organized around empowered, outcome-oriented teams — and is scaling its data and AI capabilities to serve customers and run the business better. Voyager is one of these product pods, spanning our Life, Annuities, and Employee Benefits lines.

The DataOps/MLOps Lead owns the platform and operating model that lets data and ML engineers ship reliably. You build the paved paths — CI/CD, orchestration, observability, environments, and governance automation — on our Databricks Lakehouse on Microsoft Azure, so that pipelines and models move from development to governed production quickly, safely, and repeatably.

This is a hands-on technical leadership role: you set standards, automate the operational backbone, mentor engineers, and are accountable for the reliability, security, cost, and auditability of the pod's data and ML systems in a regulated insurance environment.

KEY 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.