- Design and develop scalable data pipelines to process and transform large volumes of structured and unstructured data.
Data Processing
- Build and optimize data processing frameworks using Apache Spark.
Data Integration
- Integrate data from multiple sources to create reliable and efficient data workflows.
Data Management
- Ensure efficient storage, retrieval, and transformation of data for analytics and reporting.
System Performance
- Monitor and optimize data pipelines for performance, scalability, and reliability.
Troubleshooting
- Identify and resolve issues in data pipelines and processing systems to ensure data accuracy and availability.
Collaboration
- Work closely with data scientists, backend engineers,
and product teams to understand data requirements and deliver solutions.
Documentation
- Maintain explicit documentation of data pipelines, data models, and workflows.
Continuous Improvement
- Stay updated with emerging technologies and best practices in big data and data engineering.
Required Skills :
- Strong proficiency with Apache Spark and distributed data processing.
- Experience designing and implementing scalable data pipelines.
- Knowledge of data integration techniques across heterogeneous sources.
- Ability to monitor, troubleshoot, and optimize data workflows for performance and reliability.
- Good communication skills for crossfunctional collaboration and documentation.
📌 Data Engineer - Apache Spark (India)
🏢 Info Edge
📍 India
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