🕒 16 days ago
Design and scale data pipelines from raw ingestion through curated analytics layers, set technical standards for data infrastructure, and mentor engineers while partnering with analytics, product, and engineering teams.
Lead DataOps / MLOps
📅 Sep 15
DataopsMlopsCI/CDAzure DevOps
📜 Description
- Own the DataOps/MLOps platform and operating model for reliable pipeline and model deployment.
- Lead CI/CD standards and pipelines in Azure DevOps for data pipelines and ML models.
- Standardize orchestration on Dagster for reusable assets and observability across pipelines.
- Operationalize the ingestion and transformation stack with automated testing and CI checks.
- Build MLOps foundations with the ML engineering team for model deployment and monitoring.
- Establish data and model observability with alerting and clear SLAs/SLOs.
🛠️ Requirements
- 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.
✨ Benefits
- Dental
- Vision insurance
- Mental health benefits and employee assistance program.
- Variety of paid time away benefits including PTO and parental leave.
- Contributions to healthcare accounts and a pension plan.
- 401(k) plan with company matching.
- ProHealth Rewards platform for wellbeing improvement and cash rewards.
Full job description
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.
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.
Employee Benefits:
We aim to protect the wellbeing of our employees and their families with a broad benefits offering. In addition to offering comprehensive health, dental and vision insurance, we support emotional wellbeing through mental health benefits and an employee assistance program. Work/life balance is important and Protective offers a variety of paid time away benefits (e.g., paid time off, paid parental leave, short-term disability, and a cultural observance day). The financial health of our employees is just as important as physical and emotional health. Some of the financial wellbeing benefits include contributions to healthcare accounts, a pension plan, and a 401(k) plan with Company matching. All employees are encouraged to protect their overall wellbeing by engaging in ProHealth Rewards, Protective’s platform to improve wellbeing while earning cash rewards.
Eligibility for certain benefits may vary by position in accordance with the terms of the Company’s benefit plans.
Accommodations for Applicants with a Disability:
If you require an accommodation to complete the application and recruitment process due to a disability, please email eric.hess@protective.com. This information will be held in confidence and used only to determine an appropriate accommodation for the application and recruitment process.
Please note that the above email is solely for individuals with disabilities requesting an accommodation. General employment questions should not be sent through this process.
We are proud to be an equal opportunity employer committed to being inclusive and attracting, retaining, and growing an inclusive workforce.
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.
• 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.
QUALIFICATIONS
REQUIRED 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.
PREFERRED QUALIFICATIONS
• Experience in financial services or insurance platform work, and familiarity with model risk and regulatory audit expectations.
• Databricks administration — Unity Catalog, cluster policies, and Mosaic AI — and familiarity with Azure Machine Learning.
• Containerization and orchestration (Docker, Kubernetes; Azure AKS or Container Apps).
• Experience with data-quality / observability tooling (e.g., Great Expectations, Monte Carlo, or similar).
• Experience automating responsible-AI and model-governance controls.
• Relevant certification such as Databricks Certified Data Engineer/ML Engineer, Microsoft Azure DevOps Engineer, or Azure Administrator.
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