Data and Analytics Architect

At Orion
180, we don’t just stand out for what we do; we shine because of how we do it. Our mission is to deliver an exceptional insurance experience through innovative technology, unparalleled customer service, and a comprehensive suite of products. Our vision is to be the premier global provider of insurance solutions and redefine the insurance industry.

O rion
180 is proud to call three vibrant cities home. Our headquarters on Florida’s stunning Space Coast in Melbourne offers an exceptional quality of life, with affordable housing, excellent transportation, and close proximity to major cities like Orlando, Tampa, and Miami. Our Irving, Texas, office places us in a dynamic center of culture, business, and entertainment, keeping us closely connected to innovation and growth.

Our Draper, Utah, location, nestled in the heart of the Salt Lake Valley, connects us to a rapidly growing tech ecosystem and provides access to the outdoor lifestyle and natural beauty the region is known for.

We believe analyticsisn'ta back-office function.

It'snot a reporting layer bolted onto the business after the real decisions get made.

It's the foundation that tells you where risk actually lives, where growth is hiding, and where you'remissing opportunities before it's too late to do anything about it.

That belief is driving real investment in our people, platforms, and in how deeply analytics is woven into the way weoperate. From underwriting to distribution to customer experience,we'rebuilding a data culture where insight drives action.

It also means evolving how we think about the role of a Data and Analytics Architect.

The expectationisn’tjust technical execution.

It’scuriosity. Ownership. The ability to step into ambiguity, frame the problem, and help guide the business toward better decisions.

In this role you will be designing and governing scalable data platforms on the Microsoft cloud. Key responsibilities include bridging the gap between complex core insurance systems (Policy Admin, Claims, Billing) and our analytical consumers, implementing data lakehouse architectures, ensuring data governance, and leading cloud migrations and platform integrations. Ideal candidates require expert-level proficiency in Azure data services and data modeling.

You will collaborate with data scientists, analysts, and business leaders to turn raw data into scalable, production-ready assets for real-time analytics engines, portfolio risk platforms, and client-facing digital products.

• Design secure, scalable, and resilient data architectures using the medallion (Bronze/Silver/Gold) pattern and Delta Lake pattern in close partnership with the Enterprise Innovation & Technology (I & T) organization.
• Lead physical and logical data modeling efforts across relational, dimensional (Kimball / Star Schema), and Data Vault methodologies.
• Develop high-performance data processing logic using PySpark, Spark SQL, and Python on Azure Databricks and Synapse Analytics.
• Define data abstraction layers ensuring third-party SaaS and legacy databases map cleanly to machine-readable schemas for efficient and measurable consumption.
• Architect secure, scalable Model Context Protocol (MCP) servers and gateways to connect LLMs and AI agents to internal analytical data repositories.
• Establish architectural patterns and best practices for data ingestion, transformation, and pipeline orchestration.
• Evaluate, benchmark, and recommend components of our modern data stack in alignment with I & T’s strategy and be an active participant/contributor to the Architecture Review Board.
• Work closely with Data Engineers to conduct architecture reviews and mentor engineers on scalability and pipeline efficiency.
• Architect semantic layer solutions and data product structures to power self-service Power BI implementations.
• Architect high-throughput batch and streaming pipelines to ingest structured transactional policy/claims data as well as unstructured data (e.g., adjuster loss notes, FNOL images, aerial and satellite imagery surveys).
• Collaborate with Data Science and Machine Learning teams to ensure clean, structured feature stores and data pipelines for…