Georgia Staffing
Senior Data Architect
A global SaaS organization with a 30+ year track record and over 250 employees worldwide is seeking a Senior Data Architect to lead the design, implementation, and evolution of enterprise-grade data systems. The company operates across North America, Latin America, and additional international markets, with a focus on delivering scalable technology solutions to clients worldwide. This is a new and highly impactful position within the engineering function. The Senior Data Architect will be responsible for building and optimizing modern cloud-based data platforms that enable advanced analytics, AI integration, and operational intelligence. The role requires deep technical expertise, hands-on implementation skills, and the ability to guide global teams through complex data challenges. Key Responsibilities
Architecture & Design Design and implement data lakes, data warehouses, and streaming/batch ETL pipelines. Define and enforce data governance, metadata management, and observability standards. Develop ontology frameworks and semantic data models for interoperability and intelligent querying. Integrate structured and unstructured data into semantic layers for AI, vector databases, and knowledge graph applications. Hands-On Implementation Build and optimize ETL/ELT pipelines using Spark, Python, and SQL. Implement data lineage tracking, schema evolution, and quality monitoring with modern tools. Develop real-time telemetry pipelines feeding analytics and AI-driven workflows. Configure and manage cloud-native data infrastructure and orchestration frameworks. Prototype and deploy agentic AI workflows with semantic data integration. Collaboration & Leadership Partner with engineering, product, and AI teams to align architecture with business goals. Mentor and upskill junior engineers, supporting global hiring and onboarding efforts. Evaluate and integrate emerging technologies such as data mesh, data fabric, and semantic ecosystems. Requirements
Required Qualifications 10+ years of experience in enterprise data architecture and engineering. Proven expertise in modern cloud data platforms (e.g., Azure Data Lake, Synapse, Databricks). Strong proficiency in Python, Spark, SQL, and semantic modeling tools. Experience with OWL, RDF, SPARQL, or equivalent semantic frameworks. Deep understanding of data lineage, data catalogs, and knowledge graph development. Track record of building scalable systems supporting analytics, AI, and operational workloads. Preferred Skills Certifications in cloud architecture (Azure, AWS, or equivalent). Background in high-growth SaaS or transformation-stage enterprises. Familiarity with data mesh, data fabric, controlled vocabularies, and linked data ecosystems. Experience with AI integration, agentic frameworks, vector databases, and semantic telemetry. Exposure to regulated industries such as Healthcare, Pharma, or FoodTech. Success Metrics Reduction in support tickets and performance bottlenecks tied to data architecture. Accelerated adoption of AI and analytics enabled by robust data infrastructure. Seamless integration of semantic models across business domains. Enterprise-wide adoption of ontology-driven data practices.
A global SaaS organization with a 30+ year track record and over 250 employees worldwide is seeking a Senior Data Architect to lead the design, implementation, and evolution of enterprise-grade data systems. The company operates across North America, Latin America, and additional international markets, with a focus on delivering scalable technology solutions to clients worldwide. This is a new and highly impactful position within the engineering function. The Senior Data Architect will be responsible for building and optimizing modern cloud-based data platforms that enable advanced analytics, AI integration, and operational intelligence. The role requires deep technical expertise, hands-on implementation skills, and the ability to guide global teams through complex data challenges. Key Responsibilities
Architecture & Design Design and implement data lakes, data warehouses, and streaming/batch ETL pipelines. Define and enforce data governance, metadata management, and observability standards. Develop ontology frameworks and semantic data models for interoperability and intelligent querying. Integrate structured and unstructured data into semantic layers for AI, vector databases, and knowledge graph applications. Hands-On Implementation Build and optimize ETL/ELT pipelines using Spark, Python, and SQL. Implement data lineage tracking, schema evolution, and quality monitoring with modern tools. Develop real-time telemetry pipelines feeding analytics and AI-driven workflows. Configure and manage cloud-native data infrastructure and orchestration frameworks. Prototype and deploy agentic AI workflows with semantic data integration. Collaboration & Leadership Partner with engineering, product, and AI teams to align architecture with business goals. Mentor and upskill junior engineers, supporting global hiring and onboarding efforts. Evaluate and integrate emerging technologies such as data mesh, data fabric, and semantic ecosystems. Requirements
Required Qualifications 10+ years of experience in enterprise data architecture and engineering. Proven expertise in modern cloud data platforms (e.g., Azure Data Lake, Synapse, Databricks). Strong proficiency in Python, Spark, SQL, and semantic modeling tools. Experience with OWL, RDF, SPARQL, or equivalent semantic frameworks. Deep understanding of data lineage, data catalogs, and knowledge graph development. Track record of building scalable systems supporting analytics, AI, and operational workloads. Preferred Skills Certifications in cloud architecture (Azure, AWS, or equivalent). Background in high-growth SaaS or transformation-stage enterprises. Familiarity with data mesh, data fabric, controlled vocabularies, and linked data ecosystems. Experience with AI integration, agentic frameworks, vector databases, and semantic telemetry. Exposure to regulated industries such as Healthcare, Pharma, or FoodTech. Success Metrics Reduction in support tickets and performance bottlenecks tied to data architecture. Accelerated adoption of AI and analytics enabled by robust data infrastructure. Seamless integration of semantic models across business domains. Enterprise-wide adoption of ontology-driven data practices.