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
