Jobs at Zartis

Leads discovery and data modeling for a canonical data platform, defining entities, relationships, semantics, measures, mappings, lineage, and architectural decisions. Analyzes complex legacy datasets, identifies quality issues and gaps, collaborates with business stakeholders and data engineers, and translates models into implementable delivery plans. The role owns the canonical model through early delivery and requires strong SQL, stakeholder engagement, documentation, and cloud data platform experience.

Design, develop, and operate scalable backend applications using Node.js, TypeScript, and AWS. Integrate Generative AI and LLM capabilities into production systems using Amazon Bedrock, RAG, tool calling, context management, prompting, and knowledge indexing. Build serverless and containerized architectures, contribute to technical decisions, and address production AI concerns including quality, latency, cost, security, traceability, observability, resilience, and maintainability.

Design, build, and maintain data ingestion and transformation pipelines into Microsoft Fabric using Python, SQL, and Azure/Fabric Data Factory. Implement Fabric medallion architecture, develop Power Platform solutions (Power Apps, Power Automate, Power BI), extend Dataverse, harden CI/CD with Azure DevOps, support governance, and collaborate with stakeholders to deliver and support data, automation, and reporting solutions.

As a Principal Software Engineer, you'll develop and ship innovative AI-driven features, enhance engineering effectiveness, and lead teams in building impactful software solutions.

Join a distributed team building a fitness-industry platform. Collaborate with designers, product and QA to design and deliver scalable services. Mentor peers, write clear Go code, improve performance and scalability, and apply Agile, DevOps and CI/CD practices to deliver high-quality software.

Design, develop, and deploy AI-powered backend applications in cloud environments. Integrate ML models and AI APIs, build CI/CD and containerized deployments, optimize solutions for scalability and reliability, and collaborate with cross-functional teams to deliver AI-driven logistics features.

Design and build scalable ETL/ELT data pipelines and a cloud-based data warehouse/lakehouse. Improve data quality, governance, and documentation. Work with stakeholders to enable analytics, BI, and machine learning by delivering clean, analytics-ready datasets and establishing data engineering best practices.

Zartis is seeking talented individuals for various roles in a distributed team focused on developing new technologies. Candidates should have good communication skills, be proactive, and experienced in building software from scratch.