Assistant Manager

Data Engineers design and build data systems and pipelines. Responsibilities include developing data processing workflows, optimizing data storage, and ensuring data accuracy. You will collaborate with data scientists and analysts to meet data requirements and resolve data issues. Strong experience in data engineering and problem-solving skills are required.

• 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 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.

• 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.