azure data engineer with fabric
6 days ago
Contractual
Hiring manager :- My profile :-
Payroll of :-
1. AZURE DATA ENGINEER WITH FABRIC
The Role : Lead Data Engineer
PAYROLL
Client - Brillio
About Role:
Experience 6 to 8yrs
Location- Bangalore , Hyderabad , Pune , Chennai , Gurgaon (Hyderabad is preferred)
Notice- 15 days / 30 days.
Budget -15 LPA
AZURE FABRIC EXP MANDATE
Skills: Azure Onelake, datapipeline , Apache Spark , ETL , Datafactory , Azure Fabric , SQL , Python/Scala.
Key Responsibilities:
- Data Pipeline Development: Lead the design, development, and deployment of data pipelines using Azure OneLake, Azure Data Factory, and Apache Spark, ensuring efficient, scalable, and secure data movement across systems.
- ETL Architecture: Architect and implement ETL (Extract, Transform, Load) workflows, optimizing the process for data ingestion, transformation, and storage in the cloud.
- Data Integration: Build and manage data integration solutions that connect multiple data sources (structured and unstructured) into a cohesive data ecosystem. Use SQL, Python, Scala, and R to manipulate and process large datasets.
- Azure OneLake Expertise: Leverage Azure OneLake and Azure Synapse Analytics to design and implement scalable data storage and analytics solutions that support big data processing and analysis.
- Collaboration with Teams: Work closely with Data Scientists, Data Analysts, and BI Engineers to ensure that the data infrastructure supports analytical needs and is optimized for performance and accuracy.
- Performance Optimization: Monitor, troubleshoot, and optimize data pipeline performance to ensure high availability, fast processing, and minimal downtime.
- Data Governance & Security: Implement best practices for data governance, data security, and compliance within the Azure ecosystem, ensuring data privacy and protection.
- Leadership & Mentorship: Lead and mentor a team of data engineers, promoting a collaborative and high-performance team culture. Oversee code reviews, design decisions, and the implementation of new technologies.
- Automation & Monitoring: Automate data engineering workflows, job scheduling, and monitoring to ensure smooth operations. Use tools like Azure DevOps, Airflow, and other relevant platforms for automation and orchestration.
- Documentation & Best Practices: Document data pipeline architecture, data models, and ETL processes, and contribute to the establishment of engineering best practices, standards, and guidelines.
- C Innovation: Stay current with industry trends and emerging technologies in data engineering, cloud computing, and big data analytics, driving innovation within the team.C
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