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Form of workJob Description
Responsibilities:
1. Work in big data. Take existing solutions, optimize and build high-performance algorithms. Scale algorithms on terabytes of data. Improve time complexity and space complexity of data pre-processing. Recommend ways to improve data reliability, efficiency and quality.
2. Process structured and unstructured data, validate data quality, help to design data quality tests in big data environment.
3. Help to develop and support data products. Develop data set processes for data modeling, mining and production.
4. Work closely with engineers and data scientists. Help data science team and engineers to improve spark performance. Be involved into data products deployment.
5. Create custom software components using Spark or PySpark (e.g. specialized UDFs) and analytics applications.
6. Help to create visualization tools for tracking model performance and data quality. Integrate new data management technologies and software engineering tools into existing structures.
7. Collaborate with data architects, engineers, data scientist, business team members on project goals.
Minimum qualifications:
- BS degree in a quantitative field such as statistics, operations research, computer science, mathematics, physics, electrical engineering, industrial engineering.
-2 years of relevant work experience in big data analysis or related field (Data Engineer/developer).
-Expert in Spark, Python/ or R, PySpark/ or SparkR, Scala, SQL/Hive
- Familiar with Spark MLlib, SparkSQL
-Accomplished in Hadoop-based data mining frameworks.
Preferred qualifications:
- MS or PhD degree in a quantitative field.
- 2-3 years of relevant work experience, including deep expertise in Spark.
