Senior Data Engineer(6+ years with MS Fabric, Azure, ADF, Data Platforms only)

Job Summary We are looking for a Senior Data Engineer to design, build, and operate modern data solutions that power our data products, analytics, integrations, reporting, and emerging AI capabilities. This is a hands-on engineering role for someone who combines strong data engineering fundamentals with experience in modern cloud data platforms, APIs, integration patterns, and production-grade engineering practices. You will work across technologies such as Databricks, Microsoft Fabric, Snowflake, and cloud-native services, depending on the needs of the solution. We value engineers who understand the underlying architecture and engineering principles rather than being tied to a single technology. We are also looking for an AI-first engineer who actively uses AI-assisted development to improve how solutions are designed, developed, tested, documented, and supported while maintaining strong engineering quality and accountability. Key Responsibilities Build Modern Data Solutions Design, develop, and maintain scalable data pipelines supporting operational, analytical, reporting, and AI use cases. Build batch, incremental, near-real-time, and event-driven data processing solutions. Develop data ingestion, transformation, orchestration, and delivery workflows using modern cloud data platforms. Design and implement data models optimized for performance, maintainability, and consumption. Develop reusable engineering components and patterns rather than one-off solutions. Optimize pipelines and data processing for reliability, scalability, performance, and cost. Contribute to technical design discussions and architecture decisions. Build Enterprise Data Integrations Design and implement integrations across enterprise applications, data platforms, and third-party systems. Develop reusable integration services and data-access capabilities. Build secure REST and GraphQL APIs to expose data and platform capabilities. Implement event-driven and asynchronous integration patterns where appropriate. Integrate systems using APIs, connectors, messaging, events, and modern data-sharing techniques. Apply appropriate authentication, authorization, versioning, monitoring, and API lifecycle practices. Engineer for Quality & Reliability Build automated data validation, reconciliation, and quality controls into pipelines and data products. Implement appropriate unit, integration, and data testing as part of development. Build monitoring and observability into pipelines, APIs, and integration services. Troubleshoot complex production issues and perform root-cause analysis. Implement preventive improvements rather than repeatedly addressing symptoms. Continuously improve the reliability, resiliency, maintainability, and performance of production solutions. Apply AI-First Engineering Practices Use AI-assisted development throughout the engineering lifecycle, including design, coding, testing, documentation, troubleshooting, and optimization.