数据运维/机器学习运维负责人
Lead DataOps / MLOps
我们的工作影响着数百万人的生活，你可以成为其中一员。
我们帮助客户抵御生活中的不确定性。无论你在公司哪个部门工作，你都在客户最需要的时候提供保障和安心。
Protective Life正在转变软件的构建和运营方式——转向以赋权、结果导向的团队为核心的产物运营模式——并扩大其数据和AI能力，以更好地服务客户和运营业务。Voyager是这些产品小组之一，涵盖我们的寿险、年金和员工福利业务线。
DataOps/MLOps负责人负责让数据和机器学习工程师能够可靠地交付的平台和运营模式。你将在微软Azure上的Databricks Lakehouse上构建便捷路径——CI/CD、编排、可观测性、环境和治理自动化——使管道和模型从开发快速、安全、可重复地进入受控生产环境。
这是一个亲力亲为的技术领导职位：你制定标准，自动化操作基础架构，指导工程师，并对在监管保险环境中该小组的数据和机器学习系统的可靠性、安全性、成本和可审计性负责。
员工福利：
我们旨在通过广泛的福利计划保护员工及其家庭的福祉。除了提供全面的健康、牙科和视力保险外，我们还通过心理健康福利和员工援助计划支持情绪健康。工作与生活的平衡很重要，Protective提供多种带薪休假福利（例如带薪休假、带薪育儿假、短期残疾和文化纪念日）。员工的财务健康与身体和情绪健康同样重要。一些财务健康福利包括医疗账户的贡献、养老金计划以及有公司匹配的401(k)计划。所有员工都被鼓励通过参与ProHealth Rewards——Protective的提升健康状况同时获得现金奖励的平台——来保护自己的整体健康。
某些福利的资格可能根据职位不同而有所差异，具体以公司福利计划条款为准。
残疾人申请人的便利措施：
如果您因残疾需要便利措施以完成申请和招聘流程，请发送邮件至 eric.hess@protective.com。此信息将被保密。
查看英文原文
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.
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.
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.