Job description

ABOUT MGT INSURANCE MGT is the first AI-driven, neo-insurer focused on evolving commercial P&C insurance for the brokers and small business owners. By combining the expertise of industry veterans with state-of-the-art technology, we are ushering in the next evolution of commercial insurance and working to move the industry forward through the use of modern technology, better processes, and a rock-star team. We seek visionary leaders who thrive in dynamic, entrepreneurial settings, and excel in autonomous roles. MGT runs its reporting on a cloud data warehouse that turns raw policy administration data into the numbers the business relies on including premium, exposure, and statutory reporting. You'll own those pipelines end to end: ingestion from our policy administration systems, the transformation layers that model the data, and the governed tables that Finance, Actuarial, Underwriting, and Reinsurance report from. This role sits at the integration layer bringing together policy administration systems, rating engines, and third-party sources into one trusted warehouse that powers reporting across the company. There's no separate QA function here. The person who builds the model is the person who proves it ties out. You'll have real autonomy over how the warehouse evolves, and direct visibility with the leaders who depend on it. What Great Looks Like - Agentic solutions let you build at 100x speed and scale - Every data load has an automated quality check that notifies the data team but resolves itself when business expertise isn't required - Business users have fully governed data with easily retrievable definitions built into the data layer - Data is an asset every user and agent — Operations, Compliance, Machine Learning — can draw on seamlessly and consistently, with human intervention reserved for edge cases and fresh integrations What You'll Do Pipeline & Infrastructure - Design, build, and maintain the SQL transformation models that carry data from raw ingestion through to the reporting layer - Maintain and extend the ingestion services that pull from our source systems, and keep them running reliably in production - Keep an eye on warehouse cost, performance, and reliability Data Modeling - Model the insurance domain — policy lifecycle, premium, coverages, limits, and exposure — at the grain the business needs to report on - Document data definitions, business logic, and the decisions behind them Data Quality - Build the agentic data quality controls that sit alongside every model, and make sure they'd actually catch a regression - Reconcile what you build against an authoritative source, and explain any variance rather than smoothing it over Partnership - Work with Finance, Actuarial, Underwriting, and Product to turn ambiguous requests into reliable, repeatable data products - Support the BI layer and the analysts who build on top of your work What You Bring - Experience with agentic-driven workflows and development - Several years building production data pipelines, with strong SQL as the core skill - Experience with a cloud data warehouse and a SQL transformation framework (Dataform, dbt, or equivalent) - Python for data tooling and integrations; comfort with APIs and semi-structured data - A git-based workflow — branches, pull requests, code review, CI - Genuine rigor about correctness — you test your own work and know how to prove a number is right - An entrepreneurial mindset, with the ability to work independently and drive projects to completion - Comfort in a fast-paced environment where priorities change frequently Nice to Have - P&C insurance domain knowledge, particularly around premium and policy lifecycle - Experience with a policy administration system (Socotra or similar), including running reporting across a legacy and a modern platform at the same time - Workflow orchestration and cloud platform fundamentals (Airflow preferred) - BI or semantic layer experience (Looker preferred) - Post-M&A or cross-vendor data integration experience How We Work - Governed sources only — there's a defined path from raw data to reporting, and we use it - Every model carries its own tests — controls ship with the code, not after it - Small, clean pull requests - Write things down — hard-won knowledge belongs in the repo, not in someone's head Location Hybrid (minimum 3 days a week in office) Denver, CO, Madison, WI, or San Francisco, CA preferred, to facilitate close collaboration with the team. Compensation The base salary range for this role is $150,000 – $200,000 annually. Compensation will depend on qualifications, experience, and location. This role is also eligible for performance-based bonus and equity, in addition to a comprehensive benefits package. We recognize that no candidate will meet every qualification and encourage individuals with relevant experience and interest in the role to apply. Please note: MGT does not accept unsolicited resumes from staffing vendors, including recruitment agencies and/or search firms. Please do not forward resumes to our jobs alias, MGT employees, or any other company location. Any submittals without a prior signed agreement will become property of MGT. OUR COMMITMENT TO DIVERSITY, EQUITY & INCLUSION At MGT, inclusion drives excellence and belonging sustains it. We believe diverse perspectives make us stronger, more innovative, and more connected to the people we serve. We value every voice, champion equity, and create space where all people can grow and succeed together. EQUAL EMPLOYMENT OPPORTUNITY STATEMENT MGT is proud to be an Equal Opportunity Employer. We do not discriminate in employment opportunities or practices based on race, color, religion, gender, gender identity or expression, sexual orientation, national origin, age, disability, genetic information, marital status, military or veteran status, or any other status protected by applicable law. All employment decisions are based on qualifications, merit, and business needs. We are committed to providing reasonable accommodations for qualified individuals with disabilities throughout the application and employment process. If you need assistance or accommodations, please contact us at accommodations@mgtinsurance.com. This job description is intended to outline the general nature and level of work performed by employees in this role. It is not an exhaustive list of all responsibilities, duties, or qualifications required. MGT Insurance reserves the right to modify, interpret, or apply this job description in any way the company deems appropriate.

Requirements

Must-haves

Pipeline & Infrastructure
Design, build, and maintain the SQL transformation models that carry data from raw ingestion through to the reporting layer
Maintain and extend the ingestion services that pull from our source systems, and keep them running reliably in production
Keep an eye on warehouse cost, performance, and reliability
Data Modeling
Model the insurance domain — policy lifecycle, premium, coverages, limits, and exposure — at the grain the business needs to report on
Document data definitions, business logic, and the decisions behind them
Data Quality
Build the agentic data quality controls that sit alongside every model, and make sure they'd actually catch a regression
Reconcile what you build against an authoritative source, and explain any variance rather than smoothing it over
Work with Finance, Actuarial, Underwriting, and Product to turn ambiguous requests into reliable, repeatable data products
Support the BI layer and the analysts who build on top of your work
Experience with agentic-driven workflows and development
Several years building production data pipelines, with strong SQL as the core skill

Nice-to-haves

P&C insurance domain knowledge, particularly around premium and policy lifecycle
Experience with a policy administration system (Socotra or similar), including running reporting across a legacy and a modern platform at the same time
Workflow orchestration and cloud platform fundamentals (Airflow preferred)
BI or semantic layer experience (Looker preferred)
Post-M&A or cross-vendor data integration experience
Governed sources only — there's a defined path from raw data to reporting, and we use it
Every model carries its own tests — controls ship with the code, not after it
Small, clean pull requests
Write things down — hard-won knowledge belongs in the repo, not in someone's head
Hybrid (minimum 3 days a week in office) Denver, CO, Madison, WI, or San Francisco, CA preferred, to facilitate close collaboration with the team.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningsqlpython

Platform: ashby
Effort: quick

Benefits

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Vision