Job description

Our client is an established growing part of the Higher Education technology industry. They are a Global SaaS Company with over 300 campus partners and 2 million students actively using their platform.

They help higher education institutions better engage their students, improve the student life experience on campus, and student success. They are committed to improving student success and college graduation rates worldwide by crafting digital experiences that build communities and increase student engagement. They have a diverse and world-class global team poised for their next phase of rapid growth.

Understanding students and institutions and their behaviours through data lie at the foundation of their work to improve student success. Every data point in their systems is important to helping them achieve their main goal. They are looking for people with a strong background in data engineering and analytics to help them design, build, scale, and maintain their data pipelines and models.

As a Data Science Engineer, you will be working with various internal teams across engineering, product, and business to help solve their data needs. Your work will directly and tangibly impact the success of millions of students across the world.

In terms of the role and responsibilities, you will:

Identify the data needs of the engineering, product, and business teams, understand their specific requirements for metrics and analysis, then build efficient, scalable, accurate, and complete data pipelines to enable data-informed decisions across the company.
Architect data pipelines and models that power internal analytics for teams, as well as customer-facing data visualization product features
Be the subject matter expert on data-driven processes, visualization workflows, and tools with which to analyze data
Build processes supporting data transformation, data structures, metadata, dependency and workload management.
Drive the collection of new data and the evolution of existing data sources, collaborate with the engineering teams to manage the product instrumentation strategies and data structures
Help the product and engineering teams understand and generalize statistical models from research efforts and help build data systems that would allow these models to be used directly in product to drive student success.
Work with the Product Manager on the squad to ensure productive, fast-moving sprints that deliver the maximum value to their customers.
Work with the other senior engineers and architects to ensure that the integration stack is reliable, flexible, and scalable.
Help to improve the team processes of the engineering team continuously.

You should:

Have at least 4 years of experience in a Data Science or Data Engineering role, with a focus on instrumenting data collection, building data pipelines, data modelling and driving insights from complex data
Have a strong engineering background and are interested in data
Care deeply about the integrity of data, have a good nose for inconsistencies in data, and be able to pinpoint the issue to ensure that the team is not making decisions based on inaccurate or incomplete data
Working experience designing data systems
Experience with data related tools such as Athena, Apache Airflow or Google Composer
Strong analytical skills to pull insights from quantitative and qualitative data sets.
Have extensive experience in a scientific computing language - Python
Experience and understanding of APIs
A successful history of manipulating, processing and extracting value from large disconnected datasets
Advanced hands-on SQL knowledge and experience working with relational databases, query authoring
Have experience building systems that process data across multiple data stores and technologies, including MySQL, Redis, Elasticsearch
The ability to set up and manage a data warehouse is a plus
Know the best practices of how different types of data should be visualized in different contexts
Have good writing and verbal communication skills