Data Engineer (Microsoft Fabric | Azure | Databricks) - RibbitZ

Overview

Ribbitz

Role Summary

Data Engineer

1. Data Pipeline Development

• Develop and maintain ETL/ELT pipelines using Azure-native tools and frameworks.
• Process structured and semi-structured data (JSON, XML, CSV, APIs).
• Implement incremental data loading and transformation logic.
• Ensure data quality, validation, and consistency across pipelines.

2. Microsoft Fabric & Lakehouse Implementation

• Work with Microsoft Fabric components including:
• + Eventstream
• + Eventhouse
• + Fabric Pipelines
• Implement Lakehouse architecture using Medallion model (Bronze, Silver, Gold).
• Utilize Delta Lake for efficient storage and processing.

3. Azure Data Platform Development

• Build data solutions using:

+ Azure Data Factory (ADF)

• + Azure Databricks
• + ADLS Gen2
• Ingest data from multiple sources:
• + ERP / SAP systems
• + APIs and external data sources
• Support cloud-based data integration and transformation.

4. Big Data Processing

• Develop data transformation workflows using:

+ PySpark And Apache Spark

• Optimize pipelines using:
• + Partitioning, caching, and efficient joins
• Work on both batch and streaming data processing.

5. Data Modeling & Analytics

• Build analytics-ready datasets for reporting and BI.
• Apply dimensional modeling concepts.
• Deliver curated datasets for downstream consumption.

6. Real-Time Data Processing (Preferred)

• Implement real-time data pipelines using:

+ Microsoft Fabric Eventstream

• + IoT / streaming data sources
• Enable real-time dashboards and monitoring use cases.

7. Analytics & Visualization Support

• Integrate data pipelines with Power BI dashboards.
• Support business teams with clean and reliable datasets.

8. Modern Data Tools & Frameworks

• Work with:
• + dbt for transformation and modeling
• + Snowflake for analytics workloads
• Develop modular, reusable, and scalable data workflows.

9. DevOps & Best Practices

• Use GitHub / Git for version control.
• Follow CI/CD and deployment best practices.
• Document data pipelines and processes.

• Python And PySpark

• SQL (Strong proficiency)
• ETL/ELT pipeline development

Cloud & Tools

• Microsoft Azure:

+ Azure Data Factory (ADF)

Data Engineering Concepts

• Lakehouse architecture
• Delta Lake
• Data modeling (basic dimensional modeling)
• Incremental data processing

Additional Tools

• dbt (preferred)
• Snowflake (good to have)
• Power BI

Experience Requirements

• 3–5 years of experience in Data Engineering
• Hands-on experience in:
• + Azure data ecosystem

+ Databricks And PySpark

Educational Qualifications

• Bachelor's degree in Computer Science / Engineering / IT
• Relevant certifications are a plus:

+ Azure Data Engineer (DP-203)

Soft Skills

• Strong analytical and problem-solving skills
• Ability to work in a collaborative Agile environment
• Good communication skills
• Attention to detail and data accuracy
• Nice to Have
• Exposure to real-time/streaming data pipelines
• Experience with API integrations
• Basic understanding of machine learning workflows
• Experience working with ERP/SAP data