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4 days ago
Job Title: Lead Data Engineering Specialist
- The role requires a professional with 6+ years of experience in data engineering or cloud computing data development.
- A key responsibility is to design, develop, and optimize ETL/ELT data pipelines using Apache Spark (PySpark or Scala), AWS Glue, and Azure Data Factory.
- Additional skills include working with structured and unstructured data to build scalable ingestion and transformation workflows across cloud platforms.
- Responsibilities also include building data lake and data warehouse solutions using AWS S3, Azure Data Lake, AWS Glue.
- Collaboration with data scientists, analysts, and application developers is crucial to support advanced analytics, reporting, and ML workflows.
- Key tasks include implementing job orchestration, monitoring, and error handling for reliable pipeline execution.
- Maintenance and support of Data Pipeline are essential responsibilities.
- Another critical task is data load monitoring.
- Data validation and quality checks must be conducted regularly.
- Identifying and optimizing ingestion pipelines in consultation with the customer is an ongoing process.
- Providing L2/L3 support for data-related issues is another key area of focus.
- Investigating Glue job failures and restarting them as needed is also an important task.
- Minor transformation logic or input data issues can be resolved promptly.
- Dependency failures in Glue workflows must be addressed.
- Tuning job configurations is crucial for optimal performance.
- Impact analysis and root cause analysis (RCA) are vital skills in this position.
- An 8x5 support model is in place to ensure seamless operations.
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