Data Engineering Expert
2 weeks ago
The ideal candidate will be responsible for designing, developing, and managing data pipelines and transformation frameworks that enable analytics, reporting, and business intelligence capabilities across the enterprise.
Key Responsibilities:- Data Pipeline Development: Design and build scalable ETL/ELT pipelines using Azure Data Factory (ADF), Azure Databricks (Spark), and Azure Synapse Analytics.
- Big Data Processing: Develop and optimize PySpark/Spark SQL jobs for large-scale batch and streaming data transformations.
- Data Ingestion: Ingest data from various sources including Apache Kafka, REST APIs, and RDBMS, ensuring real-time or near-real-time availability.
- Data Modeling: Implement data modeling strategies (star schema, snowflake schema) for analytics consumption layers in Synapse or ADLS.
- CI/CD Pipelines: Collaborate with DevOps teams to automate deployment using CI/CD pipelines (Azure DevOps, GitHub Actions, etc.).
- Monitoring and Troubleshooting: Monitor, troubleshoot, and optimize data workflows for performance, cost-efficiency, and reliability.
- Coding Standards and Version Control: Follow coding standards, participate in peer reviews, and maintain version-controlled code in Git repositories.
- Data Quality and Observability: Support data quality checks, logging, alerting, and observability mechanisms for production workloads.
- 5+ Years of Experience: Must-have 5+ years of experience in data engineering roles.
- Azure Skills: Hands-on experience with Azure Data Factory (ADF), Azure Databricks, Azure Synapse Analytics, and Apache Kafka.
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