Software Engineering Lead - Data

New

JobgetherPublic health technology

Based in the United StatesFull-TimeSenior

Salary not disclosed

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Job Details

Bring senior-level experience designing data solutions, including schema design, system integration, scalability planning, and production-ready architecture.
Have hands-on expertise with Microsoft Fabric and/or Power BI, including data modeling, reporting development, and platform architecture.
Have experience designing cloud data architectures using semantic layers, medallion architecture, and modern data warehousing patterns.
Have strong SQL skills and experience designing, developing, and optimizing data pipelines.
Have working knowledge of SQL Server performance monitoring, log interpretation, performance issues, and database reliability.
Have experience with Microsoft Fabric Data Pipelines or comparable data orchestration and workflow tools.
Have experience implementing API-based data integrations and reliable data exchange between systems.
Understand Azure data-related services, including App Services, virtual machines, Blob Storage, Log Analytics, and basic networking concepts.
Have experience with Python, Fabric notebooks, Spark, or related data-processing technologies; MLflow or model registry exposure is beneficial.
Have working knowledge of PowerShell and/or Azure DevOps dashboards and pipelines.
Be able to evaluate technical trade-offs and work effectively with engineering, database, DevOps, application, and product teams.

Design and maintain the Microsoft Fabric environment, including medallion architecture, data pipelines, and mirrored and non-mirrored databases.
Develop Power BI reporting solutions, data models, and consistent reporting standards.
Design semantic layers, data structures, and architecture patterns for future AI use cases.
Build, maintain, and improve data ingestion, transformation, and orchestration workflows.
Partner with database administrators, DevOps specialists, and application engineering teams on production-ready data solutions.
Interpret SQL Server logs and performance indicators, identify bottlenecks, and support database reliability.
Support the design and maintenance of APIs and data exchange mechanisms between applications and enterprise systems.
Monitor semantic models, pipelines, warehouses, and SQL endpoints using Azure Log Analytics and KQL.
Evaluate tools, architecture patterns, and engineering practices to improve platform maturity.
Explore AI-enabled analytics capabilities, including natural-language data querying and data-grounding approaches.

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