Principal Data Architect

Join Speria— Build Technology That Feeds the World

We’re tackling one of humanity’s biggest challenges: feeding a growing population sustainably. Our AI-powered platform gives producers real-time insight to boost yield, improve animal welfare, and protect the planet.

Join us and build technology that truly impacts the world.

As a Principal Data Architect, you will lead the design, implementation, and evolution of enterprise-grade data systems. You will architect modern data platforms, semantic layers, telemetry pipelines, and agentic AI orchestration that power analytics, AI, and operational excellence. Your work directly shapes how data flows across the business and enables intelligent, scalable, and secure systems that support global customers.

• Architect unified data models supporting modular monoliths and microservices-based platforms.
• Design and implement data lakes, data warehouses, and streaming/batch ETL pipelines using Databricks, SQL Server, Azure Synapse, and Delta Lake.
• Define data governance, metadata management, and observability standards.
• Develop ontology frameworks using OWL, RDF, SPARQL for semantic interoperability.
• Integrate structured and unstructured data into semantic layers for AI and analytics.
• Build and optimize high-volume ETL/ELT pipelines using Spark, Python, SQL.
• Implement data lineage, schema evolution, and data quality monitoring.
• Develop semantic telemetry pipelines for real‑time analytics and AI agents.
• Manage cloud-native data infrastructure including Azure Data Factory, Event Hubs, Blob Storage.
• Prototype agentic AI workflows using orchestration frameworks.

• 10+ years experience in enterprise data architecture and engineering.
• Expertise with SQL Server, Databricks, Azure Data Lake, Synapse, Purview.
• Strong proficiency in Python, Spark, and semantic modeling tools.
• Hands‑on experience with OWL, RDF, SPARQL.
• Deep understanding of data lineage, catalogs, knowledge graphs.
• Bachelor’s degree.

• Certifications in cloud architecture (Azure, AWS), data modeling, semantic technologies.
• Regulated industries (AgTech, Food Tech, Pharma, Healthcare).
• Controlled vocabularies, taxonomies, linked data.
• High-growth SaaS environments.
• Agentic AI frameworks, vector databases, semantic telemetry.

• Reduction in support tickets and performance bottlenecks tied to data architecture.
• Seamless integration of semantic models across business domains.
• Acceleration of AI/analytics initiatives through strong data infrastructure.
• Adoption of ontology-driven architecture across teams.