Data Engineer

Data Engineer (DE) Consultant is responsible for designing, developing, and maintaining data assets and data related products by liaising with multiple stakeholders.

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.
• 5-7 years of experience in Data Engineer.
• Proficiency in programming languages commonly used in data engineering, such as Python, Pyspark, SQL.
• Experience in Azure cloud computing platform, such as developing ETL processes using Azure Data Factory, big data processing and analytics with Azure Databricks.
• Strong communication, problem solving and analytical skills with the ability to do time management and multi-tasking with attention to detail and accuracy.

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.