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Senior Director, Data Architecture & Lead Data Architect

Senior Director, Data Architecture & Lead Data Architect

Lee EnterprisesDavenport, IA, United States
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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 BigQuery.
  • 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 BigQuery, 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

    • recblid zup2dcu5fw6jghb6us29g1qxmlpzyt

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