Senior Data Engineer

Microsoft Fabric Engineering

·       Design, develop, deploy, and support enterprise data solutions using Microsoft Fabric, OneLake, Fabric Lakehouse, Fabric Warehouse, Fabric Data Factory, Fabric Data Engineering, Fabric Data Science, and Real-Time Intelligence.

·       Implement scalable medallion architectures using Bronze, Silver, and Gold layers for controlled data ingestion, refinement, conformance, and consumption.

·       Build data pipelines and reusable data products supporting operational analytics, advanced analytics, AI, and machine learning workloads.

·       Optimize Fabric capacity utilization, storage, workload placement, refresh performance, concurrency, reliability, and operating cost.

·       Establish source control, CI/CD, deployment, configuration, monitoring, observability, and support standards for Fabric environments.

Semantic Modeling and Data Product Development

·       Design and maintain enterprise semantic models that provide governed, reusable, and business-friendly definitions of data, metrics, relationships, and analytical concepts.

·       Develop reusable calculations, business rules, hierarchies, aggregations, measures, KPIs, and analytical frameworks.

·       Build semantic layers that support reporting, analytics, machine learning, AI applications, and governed self-service data consumption.

·       Optimize semantic models for scale, query performance, refresh efficiency, consistency, maintainability, security, and reuse.

·       Establish semantic model standards, ownership, versioning, documentation, testing, certification, and lifecycle governance.

Canonical Data Modeling

·       Design and maintain enterprise canonical data models that standardize shared business entities and data structures across source systems and business domains.

·       Develop conceptual, logical, and physical data models aligned with enterprise terminology and business rules.

·       Define reusable canonical entities, attributes, relationships, identifiers, event structures, and data contracts.

·       Partner with architects, data owners, stewards, engineers, and business stakeholders to resolve conflicting definitions and establish trusted data assets.

·       Reduce duplicate transformations and point-to-point mappings by creating reusable canonical integration and analytical structures.

Azure Data Factory and Enterprise Integration

·       Architect, build, and maintain Azure Data Factory and Fabric Data Factory solutions for enterprise-scale ingestion, transformation, movement, and orchestration.

·       Develop ETL and ELT pipelines, incremental processing, Change Data Capture, event-driven integrations, streaming patterns, and resilient recovery workflows.

·       Integrate data from databases, files, APIs, SaaS applications, cloud services, on-premises platforms, and third-party systems.

·       Create reusable ingestion frameworks, metadata-driven pipelines, parameterized components, and integration accelerators.

·       Implement secure connectivity, secrets management, data validation, error handling, logging, alerting, observability, and automated recovery.

AI, Machine Learning, and Data Science Enablement

·       Create curated, governed, and AI-ready datasets for data scientists, machine learning engineers, AI developers, analysts, and intelligent applications.

·       Integrate Microsoft Fabric and Azure data services with Azure Machine Learning, Fabric Data Science, MLflow, Azure AI Foundry, Azure OpenAI, model registries, and data science workbench environments.

·       Design feature engineering and data preparation pipelines for model training, validation, testing, batch scoring, real-time inference, and model monitoring.

·       Build data architectures supporting Generative AI, Retrieval-Augmented Generation,