As a Data Engineer, you will work in various settings to build systems that collect, manage and convert raw data into usable information to be interpreted by data scientists and business analysts. The role’s goal is to make data accessible so that it can be used to evaluate and optimize business performance.   
 

Your key responsibilities will be to: 
 

• Develop and maintain scalable data pipelines as well as build new API integrations to support continuing increases in data volume and complexity.
   

• Collaborate with analytics and business teams to improve data models that feed business intelligence tools and increase data accessibility.
   

• Implement processes and systems to monitor data quality, ensuring data produced is always accurate and available for key stakeholders as well as business processes that depend on it.
   

• Write unit / integration tests, contribute to engineering wiki and document your work.
   

• Perform data analysis required to troubleshoot data-related issues and assist to resolve possible relevant issues.
   

• Work closely with front-end and backend engineering teams, product managers and analysts.
   

• Define company data assets (data models), spark, sparkSQL, and hiveSQL jobs to populate data models.
   

• Design data integrations and data quality framework.
   

• Design and evaluate open source and vendor tools for data lineage.
   

• Work closely with all business units and engineering teams to develop strategy for long-term data platform architecture.
   

• Work closely with various stakeholders, including business analysts, IT team, project managers and data scientists. Enhance the firm’s Data Science and BI professionals, focusing on data workflows implementation and management that support machine learning models, large-scale analytics and BI reports.