Data Solutions Architect

Experience Level Senior-Lead Level (10 or more years of data architecture and engineering experience) Role Overview The Data Solutions Architect leads the design, architecture, and implementation of enterprise-scale data platforms across Azure, AWS, and multi-cloud environments. This role defines modern data strategies, establishing robust Lakehouse and Data Warehouse solutions that support business intelligence, advanced analytics, and AI/ML initiatives. Working closely with engineering teams and executive stakeholders, the Data Solutions Architect builds scalable, secure, and performant data ecosystems to enable data-driven decision-making across the organization. Key Responsibilities Enterprise Architecture & Strategy Define enterprise data platform strategies, architectural blueprints, and engineering standards. Design scalable, secure Lakehouse and Data Warehouse architectures supporting both structured and unstructured data. Implement dimensional data models, star and snowflake schemas, and scalable ingestion frameworks. Align modern data architecture initiatives with organizational business goals and digital transformation roadmaps. Data Pipeline & Platform Engineering Architect high-performance, resilient batch and real-time data pipelines using PySpark, Databricks, Kafka, and cloud-native services. Implement end-to-end data ecosystems integrating Azure Data Factory, ADLS Gen2, Azure Synapse Analytics, Snowflake, and AWS data services. Optimize platform performance through partitioning strategies, clustering, data skew remediation, schema evolution, and distributed workload tuning. Data Governance, Security, & Quality Establish comprehensive data governance, metadata management, schema management, and data quality frameworks. Implement automated data validation, observability, and compliance monitoring across all data pipelines. Enforce cloud security best practices, role-based access controls, and data protection standards across multi-cloud environments. DevOps, Automation, & Analytics Integration Automate cloud infrastructure deployments using Terraform (Infrastructure as Code) and CI/CD pipelines via Azure DevOps. Integrate modern business intelligence and enterprise reporting tools, including Power BI, to deliver actionable insights. Collaborate with engineering, analytics, business, and executive stakeholders to drive architectural excellence and continuous delivery.

10 or more years of experience in data engineering, data platform architecture, and enterprise software delivery. 5 or more years of experience architecting and implementing modern cloud data platforms on Azure, AWS, or multi-cloud environments. Strong hands-on architectural experience with Azure data services, including Azure Databricks, Azure Data Factory, ADLS Gen2, and Azure Synapse Analytics. Deep expertise in Lakehouse and Data Warehouse design using Snowflake and Databricks. Extensive experience designing distributed processing pipelines using PySpark, SQL, and streaming technologies like Apache Kafka. Proven experience in dimensional modeling, schema design, and query performance optimization techniques. Experience establishing data governance, metadata catalogs, and automated data quality validation frameworks. Proficiency with Infrastructure as Code (Terraform) and CI/CD automation tools. Preferred Qualifications Experience architecting data platforms that support machine learning, advanced analytics, and AI/ML workloads. Hands-on experience with AWS data services, including AWS Glue, AWS Lambda, and S3. Advanced knowledge of enterprise reporting and semantic layer modeling using Power BI. Relevant cloud or architecture certifications (e.g., Azure Solutions Architect, Databricks Certified Architect, or Snowflake SnowPro). Core Skills & Attributes Exceptional ability to translate complex business objectives into scalable, high-performing technical architectures. Strong strategic thinking with expertise in workload optimization, cost governance, and distributed system resilience. Excellent communication and presentation skills across executive, technical, and non-technical audiences. Proven technical leadership with the ability to mentor data engineers and establish engineering best practices.