Data Quality Analytics Engineer

Canva’s Commitment and Mission
At Canva, we celebrate diversity. We deeply believe that bringing together diversity of thoughts, perspectives and expression is key to building the best product, team and company. We look for many different skills and abilities, as well as how you can enhance Canva and our culture. So, even if you don’t think you quite meet all of the skills listed or tick all the boxes, we’d still love to hear from you! 
Our mission at Canva is to empower the world to design and since launching in 2013, we have grown exponentially, amassing over 86 million monthly active users across 190 different countries and a team of over 3,000 people… and the best bit is that we’ve only achieved 1% of what we know we’re capable of. 
Join us and design your future.
About the role
The Data Quality Engineer empowers Canvanauts with the build and maintenance of Data Quality execution components used to integrate with data pipelines to monitor Data Quality. They are the engineers who understand and participate in data management processes, specifically Data Quality management.  Like a custodian, the data quality engineer creates and maintains the data quality tooling, configuration, integration and library enhancements, so that the data engineers, analytics engineers, data analysts and stewards can control and monitor data quality across the data lifecycle.
The data quality engineer applies software engineering and data management best practices on a daily basis. Their purpose is to maintain and operate the data quality framework including tooling, configuration, and integrations with other data management technical tools and processes to deliver technical data quality components of the framework.
We have a variety of systems and data processes for the purpose of relevant data collection, processing, storage and use. The data management framework aims at knowing what data we have, where it is located, what is its quality and ensuring data security. Our systems are mostly cloud-based and the technology stack is modern to suit diverse business needs helping Canvanauts to achieve their big crazy goals.

Responsibilities

• Apply data management and software engineering practices to develop, customise and maintain the data quality execution, rules and results repository, centralised to serve the purpose of data quality discovery and results store. 
• Work primarily with SQL, Java and Python to build and configure components of data quality execution and curation of DQ results and publish them.
• Follow the data ingestion, access and modeling frameworks to use them for the purpose of data preparation, discovery and enable results access through SQL and reporting, dashboarding tools like Looker, Mode. 
• Design and build out data quality facets that support answering questions from our product team and company leaders.
• Ensure that data is secure and exposed appropriately to respective groups of users.
• Integrate the Data Quality framework with the Information portal.
• Build and maintain data quality results ingestion pipeline to Snowflake using Canva standard data pipeline patterns.

About you:

• You will have at least 2 years of recent experience building and managing data quality framework components.
• You will have solid Data Analysis skills using SQL (or equivalent like Spark SQL, etc.).
• You will have hands-on experience with data wrangling using Python/Java/R.
• You will have experience in delivering product features in Agile delivery methodology.
• You will be confident in data modelling, standardisations and implementation of technical controls on data pipelines.
• You will have an appreciation and adoption of best data engineering practices.
• You will have strong hands-on experience in designing data solutions.
• You will be experienced in using CICD tools, PR processes (Github, Jira, Juniper etc.).
• You will have exposure to Snowflake, dbt, Fivetran technologies or equivalent.
• You will be able to design and build using high-level requirements.

It would be helpful if you have:

• Experience working with Data Quality tools or custom-built data quality solutions.
• Data Governance and Data Management knowledge.
• Experience working with event-oriented data sets of large size and diverse schemas.
• Experience developing dashboards and reporting systems.
• Familiarity with a wide set of data warehousing principles, including architecture, methodologies, schemas, performance/optimisation principles, and best practices.
• Curiosity and self-drive to continuously learn new techniques and tools to extract value from data.

Working at Canva 
Our culture is unlike anywhere else and we design your #CanvaLife experience to empower you to do the best work of your life.  
Whether you’re in the office, working from home or choosing your own adventure, our benefits for permanent Canvanauts include: 
• Equity packages for you to truly be a part of the Canva journey.  • We have a hybrid work model (in-office & from home), so while our offices are always open to you, we aim to come together for 8 days a year at minimum - balancing flexibility and connection • Flexible leave so you can recharge, give back, support others or focus on your own professional development. • Inclusive parental leave policy that supports all parents and carers throughout their parenting and caring journey. • An annual Vibe & Thrive allowance. This is for you to spend on whatever will support your wellbeing and development.. because you know what you need to Vibe and Thrive, better than anyone. • Virtual and in-office wellness benefits including Canva University, Employee Assistant Programs and Fitness & Meditation Classes. • Canva For Good program matching your not-for-profit donations, Force for Good leave (3 paid volunteering days) and a range of sustainability and ethical initiatives to get involved in.   
We make hiring decisions based on your experience, skills and passion. Please note that interviews are conducted virtually. When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.