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About Raydiant

Raydiant is on a mission to create amazing experiences for people everywhere they go.

Using a first-of-its-kind technology, Raydiant reimagines and transforms customer and employee experiences through dynamic and interactive digital signage. Some of the nation’s most recognizable brick-and-mortar companies, including Chick-Fil-A, First Bank, Harvard University, Thomson Reuters, and Wahlburgers use Raydiant to keep employees engaged and customers coming back, all while driving revenue.

Built with both people and businesses in mind, Raydiant focuses on the experience so companies can focus on their products. Franchise managers, IT, marketing, and communications executives can effectively scale their brick-and-mortar operations while eliminating outdated technology. Our superior product, service, and integrations seamlessly create more engaging and personalized in-store experiences that keep customers coming back and buying more.

About the role

Our team is great, but you can help make it better.

Join a team of Data Engineers to build the next-gen data platform at Raydiant; collaborate, manage, coach, and develop the members of the group to ensure that it is both effective and efficient in its mission. Fully hands-on position as Data Engineer.

The role is remote, but ideally, candidates will be based in the Vancouver area, where Raydiant already has a presence.

What You Will Be Doing

• Hands-On data engineer and analyst to implement and verify production machine learning algorithms and pipelines
• Design, build, and maintain data pipelines for both batch and real-time processing in a processing environment
• Maintain and optimize the data infrastructure required for ETL workloads
• Develop new ETL processes to help extract and manipulate data from multiple sources
• Automate data workflows such as data ingestion, aggregation, and ETL processing
• Process raw data in Data Warehouses into a consumable dataset for both technical and non-technical stakeholders
• Partner with data scientists and functional leaders in sales, marketing, and product to deploy machine learning models in production
• Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, GCP)
• Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures
• Monitor data systems performance and implement optimization strategies
• Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership
• Create, qualify, and manage Big Data opportunities through to the completion
• Build and maintain strong links with other engineering members, and product management to enable both understand and influence the company’s technology strategy
• Develop approaches to establish, validate, and extend complex datasets used to train and evaluate statistical models

What We Are Looking For

• Bachelor's degree in Computer Science or related field

• A minimum of 5 years hands-on experience as a Data Engineer

• Formal qualifications in one of the following disciplines Computer Science, Data Science, Statistics, Mathematical Modelling

• Proven skill development and applying machine learning or algorithms to large-scale business problems.

• Advanced SQL skills and experience with relational databases and database design

• Experience working with cloud Data Warehouse solutions (e.g., Redshift, BigQuery, Snowflake, Databricks, etc.)

• Experience working with data ingestion tools such as Kinesis Firehose, Kafka, Stitch, or Matillion

• Working knowledge of public clouds (e.g. AWS, GCP)

• Experience building and deploying data lakes

• Experience building and deploying machine learning models in production

• Strong proficiency in object-oriented languages: Python, Go

• Strong proficiency in scripting languages like Bash

• Strong proficiency in data pipeline and workflow management tools (e.g., AWS Glue, Airflow)

• Experience working with IaC frameworks such as Terraform, AWS CDK

• Good understanding of NoSQL databases like Redis, Cassandra, MongoDB, and DynamoDB

• Expertise with popular off the shelf machine learning toolkits and APIs

• Experience with R&D agile methodologies

• Experience with CI/CD pipelines

• Strong project management and organizational skills

• Excellent problem-solving, communication, and organizational skills

• Ability to explain/evangelize machine learning and statistical concepts to software engineers, project managers, team members, executives, and technical managers

• Proven ability to work independently and with a team

• Agile, enthusiastic self-starter who takes pride in being on top of the technology bleeding edge and recognized as a knowledge source within the community

• Experience working with multiple customer and partner data sets
• Experience building customer-facing data products