Microsoft Fabric & Data Platform Development

- Design, develop, and maintain end-to-end data engineering solutions using Microsoft Fabric.
- Build and manage Fabric Data Factory pipelines for batch, scheduled, and incremental data ingestion.
- Develop and maintain Fabric Lakehouse and Warehouse solutions, using OneLake for centralized data storage and management.
- Use Fabric notebooks, Spark, SQL analytics, and Dataflows Gen2 to process and transform data.
- Apply Medallion Architecture (Bronze, Silver, and Gold) where appropriate for data organization, quality, and reuse.

Data Ingestion, Transformation & Modeling

- Develop robust ETL/ELT processes to ingest data from databases, REST APIs, files, and cloud/SaaS applications.
- Implement incremental loads, change data capture (CDC), slowly changing dimensions (SCD), partitioning, and other suitable processing strategies.
- Develop and optimize analytical data models, including dimensional models and star/snowflake schemas.
- Use SQL, Python, PySpark, and Spark SQL to transform, validate, and prepare data for reporting and analytics.
- Implement data quality checks, validation, reconciliation, error handling, and appropriate recovery mechanisms.

Performance, Reliability & Operations

- Monitor pipeline execution and data processing; troubleshoot failures and perform root-cause analysis.
- Optimize Spark jobs, SQL queries, pipeline design, and storage usage for performance, scalability, and cost efficiency.
- Implement logging, monitoring, alerting, and production-support practices to improve platform reliability.
- Support issue resolution and continuous improvement across data pipelines and platform workloads.

Security, Governance & Deployment

- Apply appropriate security controls, workspace access, identity and access management, and data access practices with platform and security teams.
- Follow data governance, documentation, and engineering standards; support data lineage and governance requirements where applicable.
- Use Git-based source control and support CI/CD and deployment processes for Fabric solutions through Azure DevOps or GitHub.




- Document data architecture, pipelines, data models, dependencies, operational procedures, and technical decisions.

Work with Power BI teams to provide curated, trusted, analytics-ready datasets and support semantic-model requirements.

- Collaborate with analysts, data scientists, application teams, and business stakeholders to understand requirements and deliver practical solutions.
- Contribute to design reviews, estimation, sprint planning, testing, release activities, and continuous improvement in an Agile delivery environment.

Experience & Education

- 3+ years of experience in data engineering, with mandatory hands-on experience developing solutions in Microsoft Fabric.
- Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field; equivalent professional experience may be considered.
- Experience building end-to-end data pipelines and supporting data solutions through development, deployment, and production.

Microsoft Fabric Skills

- Hands-on experience with Fabric Data Factory / Data Pipelines, Lakehouse, Warehouse, OneLake, notebooks, Spark, SQL analytics, and Dataflows Gen2.
- Understanding of Microsoft Fabric architecture, workspaces, and end-to-end data processing patterns.
- Ability to develop, troubleshoot, and optimize production-ready data pipelines in Microsoft Fabric.
- Familiarity with Fabric data agents and their potential use in data discovery or conversational access to data.

Core Data Engineering Skills

- Strong SQL skills, including complex queries, joins, CTEs, window functions, and query optimization.
- Robust programming knowledge in Python and/or PySpark,



with practical experience in data transformation and processing.
- Solid understanding of ETL/ELT concepts, batch and incremental processing, data warehousing, and dimensional modeling.
- Experience with data quality, validation, reconciliation, error handling, and reliable pipeline design.
- Working understanding of cloud security, identity, access management, and secure handling of enterprise data.

DevOps & Engineering Practices

- Experience with Git and source control, and familiarity with Azure DevOps or GitHub.
- Understanding of CI/CD pipelines, automated deployment, environment management, and release practices.
- Experience with monitoring, logging, troubleshooting, and production support.

Microsoft Certified: Fabric Data Engineer Associate (DP-700) is preferred; Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) is also relevant.

- Experience migrating data solutions from Azure Synapse Analytics, Azure Data Factory, Databricks, or traditional data warehouses to Microsoft Fabric.
- Experience with Power BI semantic models and DAX.
- Knowledge of Microsoft Purview, data governance, data lineage, and compliance practices.
- Exposure to real-time analytics, Eventstream, or Real-Time Intelligence in Microsoft Fabric.
- Experience integrating REST APIs and SaaS data sources.
- Familiarity with Microsoft Azure AI Foundry / Microsoft Foundry and its data or AI-related capabilities.
- Experience working in Agile/Scrum teams.

Strong analytical thinking, problem-solving, and attention to data quality.

- Able to translate business requirements into scalable and maintainable data solutions.
- Takes ownership of data engineering solutions from design and development through deployment and production support.
- Shows initiative in learning and applying evolving Microsoft Fabric and modern data engineering capabilities.

Improved data quality, reconciliation, and availability of trusted analytics-ready datasets.

- Effective pipeline and query performance, with appropriate cost and resource optimization.

📌 Data Scientist / Analytics Engineer (Vadodara)
🏢 netweb software
📍 Vadodara