Senior Director, Data Architecture & Lead Data Architect

Lee Enterprises is seeking a transformational Senior Director, Data Architecture & Lead Data Architect to define and deliver the enterprise data strategy that underpins our digital transformation. This leader will design, implement, and optimize a unified data architecture that enables data-driven decision-making across our business.

Reporting directly to the VP of Application Development & Chief Architect, this role combines leadership, architectural vision, and hands-on technical expertise. You will be responsible for developing the company's data strategy, modernizing data platforms, managing infrastructure, and ensuring governance and analytics readiness to support advanced capabilities including AI/ML.

You will also lead and mentor the Data Services team, ensuring delivery excellence across pipelines, integrations, governance, architecture design, and analytics enablement.

Supervisory Responsibilities

• Provide direct leadership of the Data Services team, including architects, engineers, analysts, and contractors.
• Oversee hiring, professional development, mentorship, and performance management to build a high-performing, outcomes-driven organization.

Essential Duties and Responsibilities

• Define and lead the enterprise Data Strategy and Improvement Program, aligning to business and technology goals.
• Document and design the current-state data architecture (sources, pipelines, lakes, warehouses, reporting tools, governance practices).
• Architect and maintain scalable, secure, and high-performing data systems including design and implementation of ELT pipelines using Apache Airflow, with transformations orchestrated in DBT and executed in Big Query.
• Lead the integration and normalization of source systems built on MS SQL Server, Oracle, and Progress into the centralized data lake.
  Design and implement data integrations across enterprise projects, including subscription, order management, and ERP system replacements.
• Build and maintain ELT pipelines using Apache Airflow, DBT, and modern data orchestration tools.
• Drive adoption of master data management (MDM) and establish enterprise definitions for core data domains.
• Define and enforce enterprise-wide data governance, lineage, quality, and cataloging practices.
• Forge strategic partnerships with Business Intelligence, Audience, and Advertising leaders to align architecture to use cases and business outcomes.
• Collaborate with product, engineering, and analytics teams to make data accessible, reliable, and actionable.
• Define and enforce data governance, lineage, cataloging, and quality standards enterprise-wide.
• Partner with internal stakeholders on building Single Account Record (SAR) models and productizing datasets.
• Establish data observability and monitoring practices for pipeline reliability, SLA adherence, and operational resilience.
• Implement SDLC best practices-using Agile sprints, JIRA, and roadmap-based planning-to manage workload, demand, and prioritization with transparency.
• Evangelize best practices in modern data stack, semantic layer design, and domain ownership.
• Support modernization by retiring legacy BI tools and transitioning to cloud-native, AI-ready architectures (retirement of legacy BI tools like SSRS, Crystal Reports, or Pickaxe)
• Stay abreast of emerging trends in open-source data tools, cloud data platforms, AI/ML-ready design, and federated data architectures.

Required Skills and Experience

• Expert-level proficiency with Google Big Query, DBT, and Apache Airflow.
• Deep experience with SQL Server, Oracle, and Progress database systems.
• Strong hands-on skills in data architecture, data modeling, and performance optimization.
• Demonstrated success leading and scaling a data team.
• Experience designing and governing data lakes, data warehouses, and domain models in a federated or centralized architecture.
• Familiarity with data mesh or data-as-a-product principles.
• Proficiency with Git-based workflows, CI/CD pipelines, and modern data versioning tools.
• Excellent written and verbal communication, including the ability to explain technical concepts to non-technical audiences and to engage both executive and technical stakeholders.

Preferred Qualifications

• Experience preparing data platforms for AI/ML, including dataset readiness for LLMs and retrieval-augmented generation (RAG).
• Background with BI tools such as Metabase, Superset, or Looker.
• Familiarity with cloud infrastructure (GCP preferred), Terraform/IaC, and Kubernetes.
• Knowledge of event-driven architectures and change data capture (CDC) strategies.
• Bachelor's or master's degree in computer science, Information Systems, Data Engineering, or related field.
• Professional certifications such as:
• Google Cloud Professional Data Engineer
• dbt Labs Certified dbt Developer
• Apache Airflow Certification (Astronomer or equivalent)
• Microsoft Certified: Azure Data Engineer Associate

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