Position: Data Engineer (Microsoft Fabric | Azure | Databricks)
Job Description Role Summary
We are seeking a Data Engineer with hands‑on experience in Azure‑based data platforms and Microsoft Fabric to build scalable data pipelines and analytics solutions. The ideal candidate will have strong skills in PySpark, SQL, and modern data engineering frameworks
, with exposure to lakehouse architecture, real‑time data processing, and cloud‑native tools
.
Key Responsibilities
- 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.
- 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.
- Azure Data Platform Development
- Build data solutions using Azure Data Factory, Azure Databricks, ADLS Gen
2.
- Ingest data from multiple sources such as ERP/SAP systems, APIs, and external data sources.
- Support cloud‑based data integration and transformation.
- Big Data Processing
- Develop data transformation workflows using PySpark / Apache Spark.
- Optimize pipelines using partitioning, caching, and efficient joins.
- Work on both batch and streaming data processing.
- Data Modeling & Analytics
- Build analytics‑ready datasets for reporting and BI.
- Apply dimensional modeling concepts.
- Deliver curated datasets for downstream consumption.
- 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.
- Analytics & Visualization Support
- Integrate data pipelines with Power BI dashboards.
- Support business teams with clean and reliable datasets.
- Modern Data Tools & Frameworks
- Work with dbt for transformation and modeling.
- Use Snowflake for analytics workloads.
- Develop modular, reusable, and scalable data workflows.
- Dev Ops & Best Practices
- Use Git Hub/Git for version control.
- Follow CI/CD and deployment best practices.
- Document data pipelines and processes.
Required Skills & Technologies Core Skills
- Python / Py Spark
- SQL (Strong proficiency)
- ETL/ELT pipeline development
Cloud & Tools
Azure Data Factory, Azure Databricks, ADLS Gen2
- Microsoft Fabric (preferred)
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 / Py Spark
- Building data pipelines and transformations
Educational Qualifications
- Bachelor’s degree in Computer Science / Engineering / IT
- Relevant certifications are a plus:
- Microsoft Fabric (DP‑600 / DP‑700)
- Azure Data Engineer (DP‑203)
- Databricks certifications
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
#J-18808-Ljbffr