Data Architect

We are seeking an experienced Data Architect with a strong background in Kimball dimensional modeling, enterprise data architecture, and industrial data platforms. This role focuses on designing scalable, analyticsready data models to support manufacturing and CPG data initiatives.

The ideal candidate will bring deep expertise in data warehouse design, star schema modeling, Azure cloud services, and industrial data standards, while collaborating closely with business, analytics, and governance teams.

Roles & Responsibilities:

• Lead the design and implementation of enterprisegrade data models, with a strong focus on Kimball dimensional modeling and star schemas
• Develop and maintain conceptual, logical, and physical data models aligned with business and analytics needs
• Collaborate with Data Analysts, Business Stakeholders, and Data Governance teams to ensure model accuracy and compliance
• Architect and optimize endtoend data pipelines for efficient data ingestion, transformation, and integration
• Translate complex business and analytics requirements into scalable data architecture solutions
• Support realtime and historical analytics use cases across industrial and CPG domains

Technical Requirements:

• Core Data Architecture & Modeling Skills:

Strong expertise in Kimball Dimensional Modeling, Star Schema, Fact & Dimension Tables

Hands-on experience with Slowly Changing Dimensions (SCD Type 1, 2, 3)

Experience with Data Warehouse Design and Enterprise Data Modeling

Knowledge of Data Vault Modeling (plus)

Ability to define Conceptual, Logical, and Physical Data Models

• Cloud & Data Engineering Technologies:

Hands-on experience with Azure Data Factory (ADF)

Strong knowledge of Azure Databricks

Experience working with Azure Data Lake / ADLS

Proficiency in SQL and Python for data transformation and querying

Experience building and managing ETL/ELT pipelines

• Industrial & Domain Expertise:

Strong understanding of ISA95, Industry 4.0, and industrial maintenance strategies

Experience working with manufacturing, industrial, or CPG data

Exposure to SAP, production and operations data, and timeseries data