Data Product Architect

Role Overview

• Define end-to-end architecture for data products from source systems through analytics and downstream consumption.
• Design and govern logical, physical, and semantic data models (facts, dimensions, metrics, hierarchies).
• Apply domain-driven and data-product design principles to ensure consistency and reusability.
• Establish and govern data contracts and domain interfaces.
• Define architectural patterns across Hadoop, lakehouse, and streaming platforms.
• Guide batch, near-real-time, and event-driven designs using Spark and Kafka.
• Ensure alignment across on-prem and cloud-based platforms in a hybrid enterprise environment.
• Review and guide ingestion and data service designs built on Java/Spring Boot and Python.
• Architect Kafka-based pipelines for decoupled, event-driven data products.
• Apply graph modeling patterns where relationship-centric use cases require it.
• Define enterprise semantic models supporting BI and analytics tools (Power BI, Fabric, Tableau).
• Ensure consistent business definitions and metrics across reporting and analytics.
• Enable one-to-many consumption where a single data product supports multiple use cases.
• Embed data quality, lineage, metadata, and observability into architectural designs.
• Partner with centralized governance, security, and risk teams to meet regulatory requirements.
• Define data product ownership, stewardship, and lifecycle standards.
• Act as the architectural authority for data products within the organization.
• Review and approve solution designs and reference implementations.
• Bridge enterprise architecture standards with delivery execution across teams.

Requirements

• 12+ years of experience in data architecture, data engineering, or analytics architecture.
• Proven experience designing enterprise-scale data products and platforms.
• Strong expertise in data modeling, lakehouse architectures, and streaming systems.
• Excellent communication skills with technical and business stakeholders.
• Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
• Proficiency in Data Platforms: Hadoop, modern lakehouse architectures.
• Experience with Streaming & Processing: Spark, Spark Streaming, Kafka.
• Programming skills: Java, Spring Boot (design/review), Python.
• Knowledge of Modeling & Analytics: Dimensional, canonical, domain-driven modeling; semantic layers.
• Familiarity with Observability: Data observability and operational monitoring (ELK preferred).
• Understanding of Governance & Security: Data governance, lineage, quality, and compliance.

Tech Stack

• Cloud
• Hadoop
• Java
• Kafka
• Python
• Spark
• Spring
• Spring Boot
• SpringBoot
• Tableau