Data and Analytics Architect

Orion180•Irving, TX

3d•Onsite

About The Position

We believe analytics isn't a back-office function. It's not 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're missing 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 we operate. From underwriting to distribution to customer experience, we're building a data culture where insight drives action. It also means evolving how we think about the role of a Data and Analytics Architect. The expectation isn’t just technical execution. It’s curiosity. Ownership. The ability to step into ambiguity, frame the problem, and help guide the business toward better decisions.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field.
United States Citizen or Green Card holder required.
7+ years in enterprise data architecture with demonstrated expertise in leading data platform initiatives and modernizing data estates.
Expert in Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, Azure Data Factory, and Microsoft Fabric.
Strong knowledge of relational and NoSQL systems like Azure SQL Database and Azure Cosmos DB.
Experience designing real-time architecture using Azure Event Hubs and Stream Analytics.
High proficiency in Python, SQL, PySpark, and Spark SQL.
Ability to integrate Geospatial data types and visualization platforms for location intelligence that removes data fragmentation and technical bottlenecks.
Excellent analytical, problem-solving, and critical-thinking skills.
Strong communication and collaboration abilities across technical and non-technical teams.
Ability to manage multiple projects in a fast-paced, results-driven environment

Nice To Haves

Microsoft Certified: Azure Data Engineer Associate or Azure Solutions Architect Expert.
Experience or familiarity with semantic data modeling and governed metric layers.
Working within scalable cloud data platforms (e.g., Snowflake, Databricks).
Integrating analytics within broader data engineering workflows.
Exposure to emerging capabilities such as AI-assisted analysis, automated insight generation, and advanced data visualization techniques.

Responsibilities

Designing and governing scalable data platforms on the Microsoft cloud.
Bridging the gap between complex core insurance systems (Policy Admin, Claims, Billing) and our analytical consumers.
Implementing data lakehouse architectures.
Ensuring data governance.
Leading cloud migrations and platform integrations.
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 future ML models and applications.
Implement automated data quality monitoring, data lineage tracking, and metadata management frameworks across the data lifecycle.
Partner with the Enterprise Technology and Security and Compliance teams to enforce role-based access control (RBAC), data masking, and compliance standards (SOC 2, GDPR, CCPA).
Partner with the I & T architecture team to build and optimize complex ETL/ELT workflows utilizing Azure Data Factory and Microsoft Fabric Data Pipelines.
Work closely with Data Engineers, Data Scientists, and business stakeholders to deploy AI/ML models and Business Intelligence solutions (e.g., Power BI).