Lead DataOps / MLOps

Protective Life

. Own the DataOps/MLOps platform and operating model, including paved paths, automation, and tooling for data and ML engineers .

Posted 9/22/2026full-timeRemote • United StatesSenior💰 $124,500 - $170,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in DataOps and MLOps, focusing on CI/CD standards, Azure DevOps pipelines, and orchestration with Dagster. Proficient in managing Databricks Lakehouse, implementing observability practices, and ensuring compliance and governance in data and ML operations.

Highest-signal resume keywords

DataOps/MLOps Platform OwnershipCI/CD Expertise with Azure DevOpsDagster Orchestration ExperienceMLOps with MLflow and DatabricksInfrastructure-as-Code Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLCI/CD StandardsData Ingestion with DLTDBT ModelingDatabricks LakehouseMLflow Model RegistryTerraformObservability/Monitoring ToolingIncident Management

Soft Skills

Technical LeadershipMentoring EngineersContinuous Improvement

Tools & Technologies

Azure DevOpsDagsterDatabricksUnity CatalogDockerKubernetesAzure AKSGreat ExpectationsMonte CarloAzure Machine Learning

Certifications & Qualifications

Relevant Databricks CertificationMicrosoft Azure Certification

Industry Keywords

Financial ServicesInsurance PlatformModel RiskRegulatory AuditData GovernanceCompliance Controls

Tech Stack

Tools & technologies

AzureCloudDockerKubernetesPythonSQLTerraformUnity

About the role

Key responsibilities & impact

Own the DataOps/MLOps platform and operating model, including paved paths, automation, and tooling for data and ML engineers
Lead CI/CD standards and Azure DevOps pipelines for data pipelines and ML models
Build, test, release, promote, and audit deployments across environments
Standardize orchestration on Dagster, including reusable assets, scheduling, backfills, dependency management, and run observability
Operationalize dlt ingestion and dbt transformation with automated testing and CI checks
Build MLOps foundations with MLflow model registry, Databricks Model Serving, deployment automation, monitoring, drift detection, and retraining triggers
Establish data and model observability for freshness, quality, lineage, latency, drift, and cost, with alerting and SLAs/SLOs
Administer and govern the Databricks Lakehouse on Azure, including workspace configuration, Unity Catalog, access controls, and policy automation
Manage infrastructure as code and reproducible development, test, and production environments
Own reliability and incident practices, including on-call, runbooks, root-cause analysis, and continuous improvement
Drive FinOps cost visibility and optimization across compute, storage, and model serving
Automate governance and compliance controls, including audit logging, inventories, approval workflows, and evidence collection
Provide technical leadership and mentor engineers on operational excellence and platform standards

Requirements

What you’ll need

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, including setting standards, building paved paths and automation, and mentoring engineers
Strong CI/CD expertise with Azure DevOps (ADO), including build/release pipelines, environment promotion, and automated testing
Git-based workflow experience
Hands-on experience with orchestration such as Dagster or equivalent
Experience with dlt (dltHub) ingestion and dbt modeling
Experience with Databricks Lakehouse and Delta Lake
MLOps experience with MLflow model registry, model deployment/serving, monitoring, drift detection, and retraining automation
Infrastructure-as-code and Microsoft Azure cloud platform administration, including compute, storage, identity, and networking basics
Terraform or equivalent experience
Strong Python and SQL skills
Experience with observability/monitoring tooling and SRE practices, including SLAs/SLOs, alerting, and incident management
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
Preferred: financial services or insurance platform experience; familiarity with model risk and regulatory audit expectations
Preferred: Databricks administration, Unity Catalog, cluster policies, Mosaic AI, and Azure Machine Learning
Preferred: Docker, Kubernetes, Azure AKS, or Container Apps
Preferred: data-quality/observability tooling such as Great Expectations or Monte Carlo
Preferred: responsible-AI and model-governance automation
Preferred: relevant Databricks or Microsoft Azure certification

Benefits

Comp & perks

Comprehensive health, dental, and vision insurance
Mental health benefits
Employee assistance program
Paid time off
Paid parental leave
Short-term disability
Cultural observance day
Contributions to healthcare accounts
Pension plan
401(k) plan with Company matching
ProHealth Rewards wellness platform with cash rewards
Annual incentive based on individual and Company performance
Remote work arrangement