Job Description

We’re seeking a Microsoft Fabric Data Engineer to design, develop, and operationalize end-to-end analytics solutions on Microsoft Fabric. You will own the data lifecycle—from ingestion and transformation to semantic modelling, warehousing, real-time analytics, and BI—leveraging One Lake, Lakehouse, Data Factory, Synapse Data Warehouse, Real-Time Analytics (KQL), and Power BI.

Experience Required

• 5-8 Years

Key Responsibilities

Data Engineering & Modeling

• Implement robust ELT/ETL pipelines with Git integration, CI/CD, deployment pipelines, and parameterization.
• Develop high-quality semantic models (Direct Lake/Import/DQ), DAX measures, and calculation groups for high-performance BI.
• Optimize storage and compute in OneLake using shortcuts, mirroring, and incremental loading strategies.

Real-Time & Streaming

• Create Real-Time Analytics solutions using KQL, event streams, and integrations with streaming services to power low-latency dashboards and alerting.

Governance, Security & Compliance

• Implement data governance with Microsoft Purview: lineage, glossary, data classification, and access policies.
• Enforce security best practices: RBAC, MS Entra ID (Azure AD) integration, conditional access, row/object-level security, and secrets management.
• Define DR/backups, cost management, and monitoring/observability (workspace metrics, pipeline runs, Spark job performance).

Operational Excellence

• Establish development standards, coding conventions, unit/integration testing, and reliability patterns.
• Lead performance tuning for Spark, SQL, and BI models; troubleshoot cross-component issues end-to-end.
• Mentor engineers and partner closely with analytics, product, and business stakeholders to translate requirements into reliable solutions.

Required Qualifications

• 5–8 years in data engineering/analytics; 3–5 years building at scale in Azure/Microsoft data stack.
• Deep, hands-on expertise with Microsoft Fabric, including:
• OneLake, Lakehouse, Delta Tables, Shortcuts/Mirroring
• Data Engineering (Spark Notebooks, PySpark/Scala/SQL)
• Data Factory (pipelines), Dataflows Gen2
• Synapse Data Warehouse (T-SQL, ELT patterns)
• Power BI (Semantic Models, DAX, Direct Lake, performance tuning)
• Real-Time Analytics (KQL, event streams)
• Strong skills in Python (PySpark), SQL/T-SQL, DAX, Git/GitHub/GitLab.
• Experience with CI/CD (Azure DevOps/GitHub Actions), deployment pipelines, and infrastructure-as-code for analytics workspaces.
• Solid understanding of data governance (Purview), security models (RLS/OLS, IAM), and cost optimization.

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Preferred/Bonus Skills

• Experience with Databricks, Azure Data Explorer, Event Hub/Kafka, REST APIs, and Python packaging for reusable transformations.
• Advanced Power BI capabilities (composite models, aggregations, calculation groups, query folding).
• Knowledge of dimension modeling, Kimball/Inmon, and data product thinking.
• Exposure to Copilot in Fabric, AI integration, and augmenting analytics workflows with LLMs.

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Education & Certifications

• Bachelor’s/Master’s in Computer Science, Data Engineering, or related field (or equivalent experience).
• Certifications (nice to have):
• Microsoft Certified: Fabric Analytics Engineer Associate
• Microsoft Certified: Azure Data Engineer Associate (DP-203)
• Microsoft Certified: Power BI Data Analyst Associate

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