Job Description

A well-established U.S.-based company that is building world-class transportation services with their range of electric vehicles, is looking for a Senior Analytics Engineer. The engineer will be responsible for converting data insights into practical suggestions for improving business strategy, decision-making, and performance. The NASDAQ-listed company is enabling users to reduce their carbon footprint with their revolutionary products and technologies. This position requires 5-6+ hours of overlap with the PST time zone. The company has successfully managed to secure more than $11.8bn in funding. 

Job Responsibilities:

• Improve data access for analytics applications by streamlining data queries, processes, and performance
• Work together with stakeholders to translate business requirements into solid data analytics solutions and architectures by first understanding what those requirements are
• Create intricate data pipelines, ETL procedures, and data models that support business objectives and analytical requirements
• To effectively extract, transform, and load data from diverse sources into data warehouses or analytical platforms, develop, deploy, and maintain ETL processes
• Throughout the ETL process, make sure data quality, integrity, and consistency
• Discover insights and trends in massive datasets by performing advanced data analysis, data mining, and statistical analysis
• Utilize predictive modeling and machine learning to tackle complicated business problems
• To successfully communicate insights and trends to stakeholders who are not technically savvy, create complex data visualizations and dashboards
• Use data visualization tools to create dynamic and useful reports (such as Tableau and Power BI)
• Deliver comprehensive analytics solutions by working with cross-functional teams that include data scientists, business analysts, and data engineers
• Processes for collecting data, procedures, and analytical approaches should be documented for knowledge exchange and future use
• Find ways to automate, optimize, and improve procedures to increase the efficacy and efficiency of analytics activities
• Concurrently manage and assign priority to a number of analytics projects to guarantee timely delivery and alignment with business requirements
• Identify and fix performance problems in data pipelines and processing.
• Instruct and mentor less experienced members of the analytics team on technical abilities and best practices