Software Engineering Lead - Data

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.

The ideal candidate will bring senior-level data engineering and solution architecture experience, strong Microsoft Fabric and Power BI skills, and the ability to design cloud-based data platforms that support both current reporting needs and future AI initiatives. You should be comfortable taking ownership, working across teams, and improving systems in a collaborative, remote-first environment.

• 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, database, DevOps, application, and product teams.

• A proactive, self-directed approach to work, with a commitment to continuous learning, accountability, honest feedback, and high-quality outcomes.

• Familiarity with Power BI Embedded or similar frameworks for integrating analytics into applications.

• Hands-on experience with Azure AI Foundry, Azure OpenAI, or emerging analytics patterns such as real-time data processing, streaming, and natural-language data querying.

• Familiarity with retrieval-augmented generation (RAG), vector databases, embeddings, AI agents, or responsible AI and governance principles.

• Experience with statistical analysis, forecasting, or health-related analytics, particularly in collaboration with data scientists or product teams.

• An interest in applying modern data and AI technologies to improve public health surveillance and decision-making.