Data Architect

Data Architect

Department: Data Solutions & Architecture

Reports To: Director, Data Solutions & Architecture

FLSA Status: Exempt

Position Summary

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.

Essential Duties and Responsibilities

Data Modeling and Design

Design conceptual, logical, and physical data models for the Microsoft Fabric lakehouse environment

Work with HIT to create dimensional models (star schemas) for the Gold layer supporting business intelligence

Define Bronze layer landing patterns ensuring source data is captured accurately and immutably

Design Silver layer transformations including entity resolution, slowly changing dimensions (SCD Type 2), and data quality flags

Establish surrogate key strategies, partition strategies, and historical data patterns

Document data models using standard notation and maintain artifacts in version control

Create data architecture for data products such as Customer Merge/Match, contract preferences, and other future solutions

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

Data Engineering and Implementation

Implement reference patterns and frameworks in Python, PySpark, SQL, and Delta Lake

Contribute production-grade code to Bronze ingestion, Silver transformation, and Gold materialization workflows

Build and maintain CI/CD gates for data quality, schema drift, naming standards, and pattern conformance

Conduct code reviews of pull requests touching the medallion architecture and shared frameworks

Partner with data engineers on complex modeling and transformation work

Data Architecture

Own and maintain the Fabric Architecture Playbook (SPOT Playbook)

Define and enforce naming conventions for lakehouses, tables, columns, and pipelines

Create reusable patterns and templates for common data scenarios

Establish data quality validation patterns at each medallion layer

Document architecture decisions using Architecture Decision Records (ADRs)

Conduct design reviews for data engineering work, ensuring alignment with standards

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

Business Partnership

Work directly with the Highlights Intelligence Team to understand reporting and analysis requirements

Translate business questions into data model designs

Partner with business domain SMEs to capture entity definitions and business rules

Collaborate with the Data Platform Tech Lead on implementation feasibility

Participate in requirements sessions and design workshops

Evangelize the value and importance of data to leaders throughout Highlights

Ensure consumption of data and insights is performed in a sustainable and best-practices manner

Data Governance — Work with data owners to:

Inform the data dictionary and business glossary entries for modeled entities

Document data lineage from source systems through Gold layer

Define data classification for tables and columns (PII, confidential, etc.)

Support data quality metric definition and monitoring

Participate in Architecture Review Board discussions

What You Should Know on Day One

Medallion Architecture: Bronze immutability principles, Silver transformation scope (entity resolution, SCD2, quality flags), and Gold business logic patterns

Dimensional Modeling: Confident designing star schemas, fact/dimension relationships, and handling slowly changing dimensions

Microsoft Fabric / Lakehouse: Working knowledge of OneLake, lakehouses, Delta tables, and how they differ from traditional data warehouses

SQL Proficiency: Ability to write and review advanced SQL for data transformations, including window functions, MERGE, CTEs, and query optimization

Python and PySpark: Production-grade experience, including Delta Lake operations such as MERGE, time travel, and schema evolution

Git Workflow: Branching, pull requests, code review, and conflict resolution

CI/CD: Pipeline development, schema validation, and automated testing of data transformations

Data Modeling Tools: Experience with ERwin, ER/Studio, or similar; comfortable diagramming data flows

Within Your First Month

Complete onboarding to the current Fabric environment and existing data models

Review and provide feedback on the Fabric Architecture Playbook

Build relationships with Highlights Intelligence Team members and understand their data pain points

Assess current Bronze/Silver/Gold implementations for consistency with standards

Within Your First Year

Establish complete logical data models for core business domains (Retail, D2C, Finance)

Document all major data flows from source systems through to Gold layer

Create a pattern library covering common scenarios (SCD, late-arriving facts, multi-source entities)

Achieve consistent adoption of naming conventions and modeling standards across new development

Reduce data-related rework by establishing clear design review checkpoints

Support successful integration of future Order Management System data

Supervisory Responsibilities: 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.

Education and Experience

Required

Bachelor’s degree in Computer Science, Information Systems, or equivalent experience

5-7 years of experience in data architecture, data modeling, or senior data engineering roles

Demonstrated experience creating logical and physical data models

Strong knowledge of dimensional modeling, star/snowflake schemas

Experience with cloud data platforms (Azure, AWS, or Databricks)

Expert-level SQL and understanding of data transformation patterns

Production-grade Python and/or PySpark, including Delta Lake operations

Experience with Git-based workflows, code review, and CI/CD pipelines

Experience documenting data architectures and creating design artifacts

Solid communication skills with ability to explain technical concepts to business stakeholders

Preferred

Experience with Microsoft Fabric, Azure Synapse, or Databricks

Knowledge of Delta Lake format and lakehouse architecture

CDMP certification or data modeling certification

Experience in retail, consumer products, publishing, or subscription business environments

Familiarity with ERP systems (NetSuite preferred)

Experience with data catalog or metadata management tools

Understanding of data governance principles

Key Competency Areas

Data Modeling

Conceptual, logical, and physical modeling

Dimensional modeling and star schema design

Slowly changing dimensions and historical patterns

Data normalization and denormalization decisions

Technical Skills

Microsoft Fabric / Azure data services

Delta Lake and lakehouse concepts

SQL (advanced)

Python and PySpark

Git workflows and CI/CD pipelines

Data modeling tools (ERwin, ER/Studio, or similar)

Architecture Practices

Architecture documentation

Design patterns and templates

Code and design review practices

Architecture Decision Records

Physical Requirements

Prolonged periods sitting or standing at a desk and working on a computer.

Work Location

On-site/in-office 3 days/month per corporate schedule.

Reasonable Accommodation Notice: Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to apply for a job or to perform your job.