Lead Data & Analytics Architect

Hylant•Birmingham, MI

7d•Hybrid

About The Position

The Lead Data Architect exists to define, own, and evolve Hylant’s enterprise application and information architecture. This role is responsible for application architecture and information architecture. This role ensures that current data initiatives are intentionally designed to meet today’s analytical needs while enabling future innovation through scalable, secure, and well‑governed platforms. The position serves as a technical authority and strategic partner across data engineering, analytics, and business teams. This role will be measured by its contribution to business outcomes such as operational efficiency, adoption of self-service data products, and AI agent effectiveness.

Requirements

Prefer bachelor’s degree in computer science, data science, engineering, or a related field, or equivalent practical experience.
Insurance industry experience, including familiarity with data domains such as policy, claims, billing, underwriting, or risk.
Six or more years of experience in data architecture, analytics architecture, or enterprise data platform design.
Demonstrated experience designing and delivering data platforms built on Azure based technologies as well as with multi-cloud or hybrid environments.
Hands on experience with DevOps (CI/CD process), event streaming and processing, AI/ML toolset, Azure Databricks, Microsoft Data Factory, Delta Lake, Unity Catalog, and Azure cloud services.
Strong experience designing analytical data models, including dimensional and star schema approaches.
Advanced SQL skills and working proficiency with PySpark for data transformation and modeling.
Experience designing and validating end-to-end data pipelines, including data quality and reconciliation processes.
Ability to clearly communicate complex technical concepts to both technical and non-technical audiences.
Experience managing a team and in establishing an Architecture Review Board, Design Reviews, and Data Standards and Conventions.
Must be willing and able to travel for in-person meetings at least on a quarterly basis
Ability and willingness to travel by car or airplane for meetings, conferences, or other business-related functions.
Must be legally authorized to work in the United States

Responsibilities

Define and drive the enterprise data architecture vision, ensuring alignment between business objectives, analytics needs, and platform capabilities (long term strategy and innovation roadmaps).
Responsible for application architecture (platform, tools, technologies) and information architecture (data models, data flow, data structures and data access)
Translate business and analytical requirements into scalable, secure, and feasible data architecture designs that support both near‑term delivery and long-term innovation.
Guide requirements gathering, backlog shaping, and solution design to ensure initiatives align with established architectural standards and future‑state roadmaps.
Lead the design of analytical data models, including dimensional and star schema designs for curated, business‑ready data layers.
Design and oversee end‑to-end data pipelines, including source‑to-target mappings, transformation logic, and serving strategies.
Establish architectural patterns and standards for data ingestion, transformation, storage, governance, and analytics consumption.
Establish and enforce data governance frameworks, standards, and policies to ensure data quality, security, and compliance.
Ensure the integrity, quality, and validation of data across the full lifecycle, from source systems through curated datasets and reporting layers.
Partner closely with data engineering and analytics teams to provide architectural guidance throughout delivery while remaining accountable for solution quality. Mentor and guide the lead data engineer.
Own the Data Strategy & Lifecycle Management, treating EDW domains as data products with SLAs, ownership, and lifecycle
Contribute to AI, Machine Learning, and Analytics Enablement Roadmap.
Responsible for overseeing, integrating, and optimizing AI agents in data pipelines and ensuring the right mix of human and AI involvement. Ensure responsible use of AI agents and automation in data integration, transformation, and delivery. Ensure responsible use of AI agents and automation in data integration, transformation, and delivery.
Understand and own relationships with external support providers and vendors as needed.
Evaluate and recommend platform capabilities and emerging technologies to continuously improve performance, scalability, and usability of the data ecosystem. Ensure solutions and system are scalable, re-usable, and cost optimized.
Perform other duties and special projects as requested.