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
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.
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.
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
