Director, Data Architecture & Governance

About this role

Our Company
Explore how you can contribute at AmeriLife.
For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.
Associates get satisfaction from knowing they provide agents, marketers and carrier partners the support needed to succeed in a rapidly evolving industry.
Job Summary
AmeriLife is seeking a highly technical, hands-on Director, Data Architecture & Governance to lead the strategy, design, governance, and evolution of the enterprise data architecture.

This leader will serve as the organization’s technical authority for enterprise data architecture, data modeling, metadata, master data management (MDM), data governance, lineage, and data quality. Working closely with Data Engineering, Data Products, AI, Reporting, and business stakeholders, the Director will define enterprise standards while actively designing scalable, cloud-native data solutions.

This is not a traditional management role. The successful candidate is expected to lead from the front by designing architectures, developing enterprise data models, reviewing technical solutions, mentoring architects, contributing to complex technical initiatives, and establishing governance that enables trusted, AI-ready enterprise data.Job Description

Key Responsibilities
Enterprise Data Architecture

• Define and own the enterprise data architecture strategy, roadmap, standards, and reference architectures.

• Design scalable cloud-native data platforms leveraging Databricks Lakehouse, Delta Lake, Unity Catalog, and modern data engineering principles.

• Lead architecture for enterprise data products, analytics, AI, and operational data platforms.

• Establish enterprise integration patterns, canonical data models, and reusable architecture frameworks.

• Conduct architecture reviews and ensure alignment across all enterprise initiatives.

• Evaluate emerging technologies and recommend architecture improvements.

Data Modeling & Information Architecture

• Lead conceptual, logical, and physical enterprise data modeling.

• Define enterprise standards for dimensional modeling, Data Vault 2.0, semantic layer modeling, canonical modeling, and master data modeling.

• Own enterprise information architecture and ensure consistency across business domains.

• Develop reusable enterprise data models and design patterns.

• Drive architecture decisions that support scalability, performance, governance, and AI readiness.

Data Governance, Metadata & Master Data

• Establish and lead the enterprise data governance framework.

• Define policies, standards, stewardship processes, and governance operating models.

• Lead enterprise metadata management, business glossary, lineage, catalog, and reference data initiatives.

• Define enterprise master data strategy, master data domains, survivorship rules, and governance processes.

• Partner with business stakeholders to improve data ownership, stewardship, and accountability.

Data Quality & Trust

• Establish enterprise data quality strategy and governance.

• Define enterprise data quality standards, scorecards, monitoring, and remediation processes.

• Drive implementation of automated quality controls within engineering pipelines.

• Improve enterprise data trust, consistency, and reliability.

Hands-on Technical Leadership

• Serve as the lead architect for complex enterprise initiatives.

• Design solution architectures and review technical designs across engineering teams.

• Develop enterprise architecture standards, reusable frameworks, and reference implementations.

• Participate in architecture reviews, code reviews, and design workshops.

• Review SQL, Spark, Python, and Databricks implementations to ensure alignment with enterprise standards.

• Prototype new technologies and validate architecture decisions through proof-of-concepts.

• Troubleshoot complex data architecture, integration, modeling, metadata, and performance challenges.

• Mentor architects and engineering teams through technical leadership and coaching.

Leadership

• Build, mentor, and lead a high-performing team of enterprise data architects and governance professionals.

• Establish architecture review boards and governance councils.

• Partner with Engineering, AI, Reporting, Infrastructure, Security, and business leaders to deliver enterprise data capabilities.

• Communicate architecture strategy and technology direction to executive leadership.

• Foster a culture of technical excellence, innovation, governance, and continuous improvement.

Required Qualifications

• Bachelor's degree in computer science, Information Systems, Engineering, or a related discipline. Master’s degree preferred.

• 10+ years of experience in enterprise data architecture, data engineering, or enterprise information management.

• 5+ years leading enterprise architecture or technical teams.

• Expert-level SQL development and query optimization.

• Strong hands-on experience with Python, Apache Spark, and modern data engineering practices.

• Deep expertise with Databricks Lakehouse, Delta Lake, Unity Catalog, and cloud-native data platforms.

• Extensive experience designing enterprise-scale data architectures on Azure or AWS.

• Expert knowledge of dimensional modeling, Data Vault 2.0, semantic layer design, canonical data models, and master data modeling.

• Extensive experience with enterprise data modeling tools such as ERwin, ER/Studio, Sparx Enterprise Architect, or equivalent.

• Experience implementing enterprise metadata management, lineage, business glossary, and data catalog solutions.

• Strong experience implementing enterprise MDM and data governance frameworks.

• Experience implementing enterprise data quality frameworks and automated quality controls.

• Experience with CI/CD, Infrastructure as Code, and modern DevOps practices.

• Excellent communication, executive presentation,…