Platform Data Architect

Speria•Atlanta, GA

1d•Hybrid

About The Position

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 Platform 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.

Requirements

Experience with Databricks, SQL Server, Azure Synapse, and Delta Lake.
Proficiency in data governance, metadata management, and observability standards.
Experience developing ontology frameworks using OWL, RDF, SPARQL.
Experience integrating structured and unstructured data into semantic layers.
Experience building and optimizing high-volume ETL/ELT pipelines using Spark, Python, SQL.
Experience implementing data lineage, schema evolution, and data quality monitoring.
Experience developing semantic telemetry pipelines.
Experience managing cloud-native data infrastructure including Azure Data Factory, Event Hubs, Blob Storage.
Experience prototyping agentic AI workflows using orchestration frameworks.

Responsibilities

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.