CANADA (REMOTE)

Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world’s professional relationship graph. Based on this data, we offer our users the insights and visibility they need to nurture and tap into their team’s network of opportunities.
This role is part of the AI Insights team, which owns the services that power Affinity’s industry-leading relationship intelligence platform. Our team extracts and retrieves information from billions of structured and unstructured data points to deliver insights to our customers.
As a Senior Data Engineer, you will collaborate with machine learning engineers, software engineers, and product managers to shape the future of private capital’s leading CRM platform. This involves designing and building scalable, efficient data extraction, load, and transform (ELT) solutions, monitoring and managing data quality, and ensuring data security and best practices.

QUALIFICATIONS:

Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply for jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role but your past experience doesn’t perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

• Design scalable and reliable data pipelines to consume, integrate and analyze large volumes of complex data from different sources, supporting the evolving needs of our business.
• Help define our data roadmap. You’ll collaborate with our team of machine learning engineers, software engineers, product, and business leaders to use data to shape product development.
• Build and maintain frameworks for measuring and monitoring data quality and integrity.
• Establish and optimize CI/CD processes, test frameworks, and infrastructure-as-code tooling.
• Build and implement robust data solutions using Spark, Python, Databricks, Kafka, and the AWS ecosystem (including S3, Redshift, EMR, Athena, Glue).
• Identify skill and process gaps within the team, and develop processes to drive team effectiveness and success.
• Articulate the trade-offs of different approaches to building ETL pipelines and storage solutions, providing clear recommendations aligned with product and business requirements.