Department: Data Solutions & Architecture
Reports To: Director, Data Solutions & Architecture
The Data Architect is responsible for designing, documenting, governing, and implementing data architecture for Highlights’ Microsoft Fabric and Azure data platforms. This role combines data architecture, data modeling, and data engineering responsibilities. Working closely with the Highlights Intelligence Team (HIT) and data engineers, the architect establishes Bronze/Silver/Gold layer standards, translates business requirements into well-designed data structures, and contributes production-grade implementations of those structures.
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Design conceptual, logical, and physical data models for the Microsoft Fabric lakehouse environment
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Work with HIT to create dimensional models (star schemas) for the Gold layer supporting business intelligence
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Define Bronze layer landing patterns ensuring source data is captured accurately and immutably
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Design Silver layer transformations including entity resolution, slowly changing dimensions (SCD Type 2), and data quality flags
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Establish surrogate key strategies, partition strategies, and historical data patterns
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Document data models using standard notation and maintain artifacts in version control
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Create data architecture for data products such as Customer Merge/Match, contract preferences, and other future solutions
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Work with HIT and other teams to establish lab environments where teams can experiment with speed and agility, while ensuring security and cost considerations are met
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Implement reference patterns and frameworks in Python, PySpark, SQL, and Delta Lake
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Contribute production-grade code to Bronze ingestion, Silver transformation, and Gold materialization workflows
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Build and maintain CI/CD gates for data quality, schema drift, naming standards, and pattern conformance
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Conduct code reviews of pull requests touching the medallion architecture and shared frameworks
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Partner with data engineers on complex modeling and transformation work
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Own and maintain the Fabric Architecture Playbook (SPOT Playbook)
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Define and enforce naming conventions for lakehouses, tables, columns, and pipelines
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Create reusable patterns and templates for common data scenarios
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Establish data quality validation patterns at each medallion layer
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Document architecture decisions using Architecture Decision Records (ADRs)
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Conduct design reviews for data engineering work, ensuring alignment with standards
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Establish and review metrics on cost, performance, usage, and value of data insights to the organization. Work with the Tech Lead and other team members to maintain appropriate ranges for these measures
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Work directly with the Highlights Intelligence Team to understand reporting and analysis requirements
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Translate business questions into data model designs
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Partner with business domain SMEs to capture entity definitions and business rules
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Collaborate with the Data Platform Tech Lead on implementation feasibility
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Participate in requirements sessions and design workshops
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Evangelize the value and importance of data to leaders throughout Highlights
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Ensure consumption of data and insights is performed in a sustainable and best-practices manner
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Inform the data dictionary and business glossary entries for modeled entities
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Document data lineage from source systems through Gold layer
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Define data classification for tables and columns (PII, confidential, etc.)
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Support data quality metric definition and monitoring
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Participate in Architecture Review Board discussions
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Medallion Architecture: Bronze immutability principles, Silver transformation scope (entity resolution, SCD2, quality flags), and Gold business logic patterns
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Dimensional Modeling: Confident designing star schemas, fact/dimension relationships, and handling slowly changing dimensions
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Microsoft Fabric / Lakehouse: Working knowledge of OneLake, lakehouses, Delta tables, and how they differ from traditional data warehouses
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SQL Proficiency: Ability to write and review advanced SQL for data transformations, including window functions, MERGE, CTEs, and query optimization
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Python and PySpark: Production-grade experience, including Delta Lake operations such as MERGE, time travel, and schema evolution
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Git Workflow: Branching, pull requests, code review, and conflict resolution
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CI/CD: Pipeline development, schema validation, and automated testing of data transformations
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Data Modeling Tools: Experience with ERwin, ER/Studio, or similar; comfortable diagramming data flows
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Complete onboarding to the current Fabric environment and existing data models
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Review and provide feedback on the Fabric Architecture Playbook
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Build relationships with Highlights Intelligence Team members and understand their data pain points
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Assess current Bronze/Silver/Gold implementations for consistency with standards
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Establish complete logical data models for core business domains (Retail, D2C, Finance)
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Document all major data flows from source systems through to Gold layer
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Create a pattern library covering common scenarios (SCD, late-arriving facts, multi-source entities)
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Achieve consistent adoption of naming conventions and modeling standards across new development
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Reduce data-related rework by establishing clear design review checkpoints
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Support successful integration of future Order Management System data
None. This is an individual contributor role. Provides architectural guidance to data engineers through code, patterns, design reviews, and collaboration, without a direct reporting relationship.
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Bachelor’s degree in Computer Science, Information Systems, or equivalent experience
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5-7 years of experience in data architecture, data modeling, or senior data engineering roles
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Demonstrated experience creating logical and physical data models
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Strong knowledge of dimensional modeling, star/snowflake schemas
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Experience with cloud data platforms (Azure, AWS, or Databricks)
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Expert-level SQL and understanding of data transformation patterns
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Production-grade Python and/or PySpark, including Delta Lake operations
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Experience with Git-based workflows, code review, and CI/CD pipelines
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Experience documenting data architectures and creating design artifacts
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Solid communication skills with ability to explain technical concepts to business stakeholders
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Experience with Microsoft Fabric, Azure Synapse, or Databricks
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Knowledge of Delta Lake format and lakehouse architecture
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CDMP certification or data modeling certification
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Experience in retail, consumer products, publishing, or subscription business environments
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Familiarity with ERP systems (NetSuite preferred)
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Experience with data catalog or metadata management tools
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Understanding of data governance principles
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Conceptual, logical, and physical modeling
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Dimensional modeling and star schema design
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Slowly changing dimensions and historical patterns
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Data normalization and denormalization decisions
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Microsoft Fabric / Azure data services
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Delta Lake and lakehouse concepts
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SQL (advanced)
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Python and PySpark
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Git workflows and CI/CD pipelines
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Data modeling tools (ERwin, ER/Studio, or similar)
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Architecture documentation
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Design patterns and templates
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Code and design review practices
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Architecture Decision Records
Prolonged periods sitting or standing at a desk and working on a computer.
On-site/in-office 3 days/month per corporate schedule.
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