What are the responsibilities and job description for the Staff Data Architect (Remote) position at KOHLS?
As a Staff Data Architect, you will serve as a senior technical leader responsible for defining and evolving enterprise data architecture to ensure data is trusted, well-governed, and scalable across domains. You will influence how data is designed and produced from the point of origination through consumption, enabling consistency, reuse, and transparency across the data ecosystem.
This role is central to solidifying our data-as-a-product mindset. The Staff Data Architect will define enterprise standards, steward canonical data models, and establish data contracts that embed governance directly into design and delivery workflows. Success in this role is measured by increased data reuse, clarity of ownership, and observable end to end data lineage, leading to higher trust in enterprise data products.
What You’ll Do
Define and steward enterprise data architecture standards including data models, data contracts, domain ownership & quality expectations, and design patterns that ensure consistency and reuse across domains.
Own and maintain enterprise data models (e.g., Customer, Product, Order, Inventory), ensuring clear definitions, documented lineage, and reuse across analytical, operational, and agentic use cases.
Influence and guide source data design by partnering with product and engineering teams to ensure data quality, ownership, and governance are embedded at the point of origination, before data propagates throughout the enterprise.
Embed governance by design by integrating metadata capture, lineage, and validation into engineering workflows through automation rather than manual review processes.
Produce and maintain architecture artifacts including data models, lineage views, integration maps, and architectural decision records. Lead design reviews to ensure architectural integrity and visibility of decisions across teams.
Influence and mentor teams across domains, helping engineers and analysts apply best practices for scalable, maintainable, and reusable data design.
Shape the enterprise data strategy by evaluating tooling and approaches for metadata management, lineage, and automation, and guiding rationalization of legacy or duplicative data assets.
Partner with product managers, software engineers, data engineers, designers, data scientists, and analytics teams to deliver scalable, reusable, and well-governed data solutions that meet business goals
Partner with enterprise architects and platform teams to align data solutions with broader cloud, analytics, and technology strategies
Additional tasks may be assigned
What Skills You Have
Required
Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent practical experience.
7 years of experience in data architecture, data engineering, or enterprise data management within large, complex environments.
Deep expertise in data modeling (conceptual, logical, and physical), including experience defining and stewarding canonical enterprise data models across domains.
Strong understanding of distributed data architectures and modern cloud data platforms (e.g., GCP, BigQuery, Kafka, Spark), with emphasis on architectural design and patterns rather than day-to-day pipeline ownership.
Experience defining and operationalizing data contracts, schema standards, and data design conventions that drive consistency, reuse, and data quality.
Hands-on experience with metadata, lineage, and governance tooling (e.g., DataHub, data catalogs, schema registries, or equivalent), and embedding these capabilities into engineering workflows.
Proficiency in SQL and Python, with sufficient technical depth to review designs, evaluate tradeoffs, and partner effectively with engineering teams.
Demonstrated ability to influence without authority, align cross-functional teams, and communicate complex architectural concepts clearly to both technical and non-technical audiences.
Proven experience mentoring engineers and architects and raising the overall quality and consistency of data design across teams.
Nice to Have
Experience working in domain-oriented or federated data ownership models (e.g., data mesh or similar patterns).
Familiarity with CI/CD-based governance, automated validation, and schema evolution strategies.
Experience supporting analytics, machine learning, or AI workloads that depend on well-modeled, trusted data.
Background in retail, e-commerce, or large-scale consumer data environments.