Analytics Engineer

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DespaQ is looking for a savvy Data Engineer who will be responsible for expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. We expect you to be excited by the prospect of optimizing or even redesigning our company’s data architecture to support our next generation of products and data initiatives. We value individuals who are self-directed and comfortable with supporting the data needs of multiple teams, systems and products.

You will

• Create and maintain optimal data pipeline architecture
• Assemble large, complex data sets that meet functional / non-functional business requirements
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics
• Work with stakeholders including the Executive, Sales, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs
• Keep our data separated and secure across national boundaries through multiple data centers and AWS regions
• Work with data and analytics experts to strive for greater functionality in our data systems

Ideally, you have

• Graduate degree in Computer Science, Informatics or relevant experience
• 2+ years of experience in a Data Engineer role or Senior Software Engineer role
• Experience with supporting and working with cross-functional teams in a dynamic environment
• Experience with building processes supporting data transformation, data structures, metadata, dependency and workload management
• Experience building and optimizing ‘big data’ data pipelines, architectures and data sets
• Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores