Data Engineer at Quacquarelli Symonds

As a Data Engineer, this is what you’ll be doing:

• As a Data Engineer at QS, you will be responsible for creating and maintaining optimal data pipeline architecture in a cloud-based environment and integrated ETL tool, assembling complex data sets, and identifying and implementing process improvements. You will also work closely with key business stakeholders to support their data infrastructure needs.
• The ideal candidate will have strong experience in data engineering and management, with expert knowledge of SQL and data modelling.

Role responsibilities:

• Create and maintain optimal data pipeline architecture in a cloud based ETL tool, using SQL and Snowflake technologies.
• Assemble potentially complex data sets that meet functional and non-functional business requirements.
• Identify, design, and implement internal process improvements, automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability.
• 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 key business stakeholders 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 centres and Snowflake/AWS regions.
• Create data tools for analytics and data science team members that assist them in building and optimizing our products/services.
• Work with data and analytics experts to strive for greater functionality in our data systems.

Key skills and experience:

• Strong Data Engineering/Data Management capabilities and experience.
• Expert proficiency in SQL.
• Experience with Snowflake/AWS/Azure or other cloud-based infrastructure.
• ETL (Extract, Transform, Load) expertise, both logically and with appropriate tools (e.g Kleene.ai, Talend, Alteryx).
• Cloud and/or Data Migration experience.
• Data Modelling expertise.

Qualifications:

• Experience as a data engineer, data analyst, or similar.
• Advanced SQL knowledge and experience working with relational databases, query authoring as well as familiarity with a variety of databases.
• Strong experience working with On-Premise and Cloud-based databases and lakes.
• Experience building and optimizing data pipelines, architectures, and data sets.
• Experience working within an Agile environment to deliver based on pre-set milestones.
• Experience building processes supporting data transformation, data structures, metadata, dependency, and workload management.
• Strong analytic skills related to working with unstructured datasets.
• Experience supporting and working with cross-functional teams in a dynamic environment.