Responsibilities:
- Work with stakeholders to understand the data requirements to design, develop, and maintain complex ETL processes.
- Create the data integration and data diagram documentation.
- Lead the data validation, UAT and regression test for new data asset creation.
- Create and maintain data models, including schema design and optimization.
- Create and manage data pipelines that automate the flow of data, ensuring data quality and consistency.
Qualifications and Skills:
- Strong knowledge on Python and Pyspark
- Expectation is to have ability to write Pyspark scripts for developing data workflows.
- Strong knowledge on SQL, Hadoop, Hive, Azure, Databricks and Greenplum
- Expectation is to write SQL to query metadata and tables from different data management system such as, Oracle, Hive, Databricks and Greenplum.
- Familiarity with big data technologies like Hadoop, Spark, and distributed computing frameworks.
- Expectation is to use Hue and run Hive SQL queries, schedule Apache Oozie jobs to automate the data workflows.
- Good working experience of communicating with the stakeholders and collaborate effectively with the business team for data testing.
- Expectation is to have strong problem-solving and troubleshooting skills.
- Expectation is to establish comprehensive data quality test cases, procedures and implement automated data validation processes.
- Degree in Data Science, Statistics, Computer Science or other related fields or an equivalent combination of education and experience.
- 3-7 years of experience in Data Engineer.
- Proficiency in programming languages commonly used in data engineering, such as Python, Pyspark, SQL.
- Experience in GCP computing platform, such as developing ETL processes
- Strong communication, problem solving and analytical skills with the ability to do time management and multi-tasking with attention to detail and accuracy.
