Description

Job Title: Digital Data Architect

Role Overview A leading organization is seeking a hands-on Digital Data Architect to design and implement modern data architectures that support analytics, AI/ML initiatives, and digital transformation. This individual contributor role emphasizes technical execution, delivering scalable, secure, and high-performing data solutions across cloud and hybrid environments.

Key Responsibilities

• Design conceptual, logical, and physical data models for both enterprise-wide and domain-specific solutions.
• Architect data platforms using technologies such as Synapse, Databricks, Data Lake, and Cosmos DB.
• Develop robust ETL/ELT pipelines utilizing Data Factory, Event Hubs, and streaming frameworks.
• Enable both real-time and batch data processing for analytics and operational systems.
• Implement data cataloging, lineage tracking, and quality frameworks with tools like Purview.
• Ensure adherence to security and regulatory standards including GDPR and CCPA.
• Optimize data storage strategies, query performance, and cost efficiency across cloud platforms.
• Collaborate with solution architects, data engineers, and analytics teams to deliver comprehensive solutions.
• Provide technical leadership and promote best practices for data platform adoption.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Information Systems, or a related discipline.
• 8+ years of experience in data engineering or architecture roles with strong hands-on capabilities.
• Expertise in cloud data platforms (preference for Azure;experience with AWS or GCP is a plus).
• Proficiency in data modeling across relational, NoSQL, and big data environments.
• Deep understanding of ETL/ELT and streaming data frameworks.
• Strong command of SQL, Python, and Spark.
• Hands-on experience with Synapse, Databricks, Data Lake, Cosmos DB, Data Factory, Event Hubs, and Kafka.
• Familiarity with business intelligence tools such as Power BI or Tableau.

Preferred Qualifications

• Experience implementing Data Mesh or Data Fabric architectures.
• Exposure to machine learning pipeline integration.
• Relevant certifications such as Data Engineer Associate or Solutions Architect Expert (Azure).

KPIs and Success Metrics

• Timely delivery of scalable data architectures.
• Measurable reduction in data processing time and improved cost efficiency.
• Enhanced data quality and governance compliance, as reflected in audit outcomes.
• Increased adoption of modern data platforms and tools across internal teams.
• Consistent achievement of service-level agreements for batch and real-time data pipelines.