Data Architect

About This Role

This role involves designing and implementing robust data architectures, primarily utilizing Databricks on major cloud platforms. The Data Architect will define end-to-end lakehouse architectures and develop best practices for data engineering and integration. This position suits a professional with strong experience in data platform design and a solid understanding of cloud-based data solutions.

Responsibilities

• Design and implement scalable, secure, and cost-effective data architectures using Databricks on Azure, AWS, or GCP
• Define end-to-end lakehouse architecture, including data ingestion, transformation, storage, processing, and consumption layers
• Develop architecture standards and best practices for data engineering, data modelling, data integration, and platform design
• Leverage Databricks capabilities, including Delta Lake, Unity Catalog, and the Medallion Architecture, wherever appropriate
• Design and optimize batch and streaming data pipelines using Apache Spark, PySpark, SQL, and related technologies
• Establish data governance, security, access control, data quality, metadata management, and lineage practices
• Collaborate with data engineers, analytics teams, business stakeholders, and cloud/platform teams to deliver reliable data solutions
• Identify and resolve architectural challenges related to scalability, performance, reliability, and cloud cost optimization
• Evaluate existing data platforms and recommend modernization, migration, and optimization strategies
• Document architecture designs, technical standards, data flows, and implementation guidelines

Requirements

• Strong experience in data architecture, data platform design, or enterprise data engineering
• Hands-on experience with Databricks and cloud-based data platforms
• Strong expertise in at least one cloud platform: Microsoft Azure, AWS, or GCP
• Solid understanding of lakehouse architecture, Delta Lake, and the Medallion Architecture
• Proficiency in SQL and practical knowledge of Python, PySpark, and Apache Spark
• Experience designing data models, data pipelines, and data integration solutions
• Understanding of data governance, security, access management, data quality, and metadata management
• Experience with performance tuning, scalability, reliability, and cost optimization of data platforms
• Ability to translate business and technical requirements into scalable architectural solutions
• Strong communication, problem-solving, stakeholder management, and technical documentation skills