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