Posted on 2021-02-24
As a Data Engineer, you will be the expert in data processing, management, and storage of data, both in the cloud and on-premise environment. You will build and design analytics ready data tables and responsible for overseeing the maintenance and storage of data. You will also build data pipelines on the backend of analytics products & solutions.
• Design and develop data pipelines for various analytics products & solutions deployed in the cloud environment or on-premise set-up.
• Build the data infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources.
• Perform data manipulation using SQL and other data management technologies to develop data assets.
• Design and implement ETL processes. Develop database solutions to store and retrieve information.
• Build data pipelines in cloud environment like Microsoft Azure and AWS.
• Assemble complex data sets that meets business requirements.
• Responsible for data gathering, data cleansing and processing and other data-related issues in a Project.
• Monitor data integrity and adopt appropriate tools.
• Evaluate data quality and completeness thereby ensuring data integrity.
• Keep data secure, organized and clean ready for analysis anytime.
• Will be working hand-in-hand with Data Scientists in solving business problems and create the appropriate data structures needed for the analysis.
• Create detailed project documentation capturing the end-to-end project process up to product delivery.
• Bachelor’s degree in a quantitative discipline like statistics, mathematics, computer science, engineering, management information system, operations research, economics or equivalent.
• Experience in data manipulation using SQL and other data management tools. Ability to create and optimize SQL queries.
• Knowledgeable on ETL processes and frameworks.
• Experience in building data solutions and pipelines in the cloud environment like MS Azure and AWS.
• Experience building and combining data sets from various sources for use as analytical tables.
• Strong experience with databases.