Biotech Data Engineer Azure Fabric
Fusemachines
IndiaremotePosted 1 month ago
Skill Required
Data-EngineeringAzure-Data-EngineeringMicrosoft-Fabric-EngineeringBiotech-Data-EngineeringCloud-Data-EngineeringBiomedical-Data-EngineerSenior-Microsoft-Fabric-EngineerSenior-Azure-Data-EngineerData EngineerAzureEngineeringObservabilityCryptographyPower BIsecuritySparkPythonDevOpsCI/CDAgileCloudETLSQLCICDAIFulltime
Key highlights
- Full-time, Remote role
- 5+ years of hands-on data engineering experience required
- Expertise in Microsoft Fabric and Azure ecosystem required
- Focus on Biotech industry and regulatory data streams
- Overlap with eastern-US-based team hours required
Role overview
Fusemachines is a 12+ year old AI company dedicated to delivering state-of-the-art AI products and solutions across diverse industries, with a mission to democratize AI and harness global talent from underserved communities. This full-time, remote role is for a Data Engineer specializing in Microsoft Fabric and the Azure ecosystem to support a major data architecture modernization for a key client in the Biotech industry. The role involves migrating an existing Azure environment (serving ~500 Power BI users) to Microsoft Fabric, optimizing performance for high concurrency, and integrating GXP manufacturing and quality data to support critical regulatory data streams.
Responsibilities
- Architect and implement the migration of the analytics environment from Azure to Microsoft Fabric.
- Integrate GXP manufacturing and quality data streams into the platform to expand capabilities for regulatory compliance.
- Implement a robust Data Quality Framework using Python and SQL to identify anomalies and ensure data integrity across the Lakehouse and Warehouse layers.
- Fine-tune and scale platform performance, data pipelines, and capacity settings to ensure optimal responsiveness for over 500 Power BI users.
- Manage and fine-tune Azure/Fabric resources, including Spark Pools, SKUs, and Capacity settings, to ensure high performance during peak times while maintaining cost-efficiency.
- Build, maintain, and optimize scalable data integration pipelines using Azure Data Factory, Azure Synapse, and Microsoft Fabric features.
- Build and enforce proactive monitoring frameworks utilizing Power BI dashboards and Microsoft Teams notifications for real-time failure alerts.
- Leverage Microsoft Fabric tools and AI capabilities, such as Copilot AI, to enhance functionality and operational efficiency.
- Work closely with project leadership and key stakeholders to maintain seamless alignment during US business hours.
Requirements
- 5+ years of hands-on data engineering experience with deep expertise in Microsoft Fabric and the broader Azure ecosystem (Azure Synapse, Azure Data Factory, Power BI).
- Advanced SQL (stored procedures, window functions) and Python/PySpark for optimized data processing.
- Proven track record of architecting and executing migrations from traditional Azure data architectures to Microsoft Fabric.
- Demonstrated experience scaling platform performance to support enterprise user bases.
- Experience integrating GXP manufacturing and quality data into cloud platforms to support stringent regulatory data streams within the Biotech / Life Sciences sector.
- Strong knowledge of modern ETL/ELT pipeline orchestration, data governance, and data lakehouse architectures.
- Deep understanding of SDLC/Agile and Azure DevOps for CI/CD and artifact management.
- Knowledge of Azure security best practices (AD, NSG, encryption) and compliance standards.
- Full working time overlap with eastern-US-based team hours.
Nice to have
- Experience or familiarity with Copilot AI.
- Certifications Preferred: Azure Data Engineer Associate, Fabric Data Engineer Associate, and Azure Solutions Architect Expert.
Additional details
- Type: Full-time, Remote
- Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.
- Originally posted on Himalayas