About the company
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineering Lead – Data based in the United States.
Join a mission-driven technology team developing modern data solutions that help public health agencies detect and prevent the spread of disease. As Software Engineering Lead – Data, you will shape and evolve a cloud-based data platform built around Microsoft Fabric, Power BI, and the broader Azure ecosystem. You will establish scalable data architectures, optimize pipelines and reporting solutions, and lay the groundwork for future AI-powered analytics capabilities. Working closely with database administrators, DevOps engineers, application teams, and data specialists, you will help deliver secure, reliable, production-ready data services. You will also champion observability, performance, and engineering standards as the platform evolves beyond traditional reporting toward a broader data and AI strategy. This remote-first opportunity combines technical ownership, cross-functional collaboration, and meaningful impact on public health outcomes.
Responsibilities
- Own and evolve the data architecture: design and maintain the Microsoft Fabric environment, including medallion architecture with bronze, silver, and gold layers, data pipelines, and mirrored and non-mirrored databases.
- Lead Power BI development: apply advanced Power BI expertise to develop effective reporting solutions, robust data models, and consistent reporting standards.
- Build AI-ready data foundations: design semantic layers, data structures, and architectural patterns that support future AI use cases and enable reliable access to enterprise data.
- Develop and optimize data pipelines: build, maintain, and improve data ingestion, transformation, and orchestration workflows using Microsoft Fabric Data Pipelines or comparable tools.
- Collaborate across technical teams: partner with database administrators, DevOps specialists, and application engineering teams to deliver scalable, production-ready data solutions.
- Support database reliability and performance: interpret SQL Server logs and performance indicators, identify potential bottlenecks, and contribute to the stability of highly available, 24/7 production environments.
- Implement API-based integrations: support the design and maintenance of APIs and data exchange mechanisms that connect applications and enterprise systems.
- Establish observability practices: monitor semantic models, pipelines, warehouses, and SQL endpoints using tools such as Azure Log Analytics and Kusto Query Language (KQL).
- Evaluate technologies and standards: assess tools, architecture patterns, and engineering practices to improve platform maturity and support a broader data and AI strategy.
- Contribute to AI-enabled analytics: explore emerging capabilities such as natural-language data querying, AI-assisted analytics, and data-grounding approaches.
- Support cloud platform operations: leverage Azure services and related tooling to maintain secure, scalable, and observable data infrastructure.
- Contribute to continuous improvement: help define technical direction, improve engineering workflows, and strengthen collaboration across a virtual-first organization.
Requirements
- Demonstrated senior-level experience designing data solutions, including schema design, system integration, scalability planning, and production-ready architecture.
- Hands-on expertise with Microsoft Fabric and/or Power BI, including data modeling, reporting development, and platform architecture.
- Experience designing cloud data architectures using semantic layers, medallion architecture, and modern data warehousing patterns.
- Strong SQL skills and experience designing, developing, and optimizing data pipelines.
- Working knowledge of SQL Server performance monitoring, including interpreting logs, identifying performance issues, and supporting database reliability.
- Experience with Microsoft Fabric Data Pipelines or comparable data orchestration and workflow tools.
- Experience implementing API-based data integrations and supporting reliable data exchange between systems.
- Solid understanding of the Azure ecosystem and data-related services, including App Services, virtual machines, Blob Storage, Log Analytics, and basic networking concepts.
- Familiarity with designing data architectures that can support AI-enabled applications and analytics.
- Experience with Python, Fabric notebooks, Spark, or related data-processing technologies; exposure to MLflow or model registry concepts is beneficial.
- General understanding of AI-related data patterns, including natural-language-to-data querying and the principles of grounding AI responses in trusted data sources.
- Working knowledge of PowerShell and/or Azure DevOps dashboards and pipelines.
- Strong analytical, organizational, and problem-solving skills, with the ability to evaluate technical trade-offs and recommend practical solutions.
- Excellent communication and collaboration skills, including the ability to work effectively with engineering, data