About Kindsight

Kindsight builds technology that helps fundraisers make a difference. For decades, Kindsight has supported the education, healthcare, and nonprofit sectors with fundraising tools and the largest charitable-giving database on the market.

As the giving sector evolves, so does Kindsight. We combine real-time data, artificial intelligence, purpose-built CRM technology, donor research, and fundraising intelligence to help thousands of organizations identify, understand, manage, and engage with donors at scale.

Our products bring donor information, campaign activity, analytics, prospect insights, and AI-powered content together to help organizations build stronger relationships and create greater impact.

Position Summary

We’re looking for a Senior Data Engineer to join our Product Engineering team and help evolve the modern data platform behind our athletics products.

Our platform processes terabytes of data and ingests hundreds of millions of records from university ticketing, constituent, CRM, merchandise, financial, and engagement systems. That data powers analytics, machine-learning scores, AI-enabled product features, reporting experiences, and integrations with client systems.

In this role, you’ll design, build, and maintain the pipelines and platform capabilities that move data reliably from complex source systems into trusted, actionable data products.

This is a hands-on engineering role with meaningful ownership across technical design, development, deployment, platform reliability, and production support. You’ll work closely with software engineers, data specialists, and product partners to solve high-volume data challenges and improve the experience of both our clients and internal engineering teams.

Our modern data stack includes Snowflake, dbt Fusion, Python, Airflow, Kubernetes, and Azure. We place a strong emphasis on sound software-engineering practices, including CI/CD, automated testing, observability, clean code, and long-term maintainability.

What You’ll Do

• Design, build, and maintain reliable ELT and ETL pipelines using Snowflake, dbt Fusion, Airflow, and Python.

• Build scalable batch and incremental ingestion processes across ticketing, CRM, constituent, merchandise, financial, and engagement platforms.

• Develop transformation workflows and reusable data products that support analytics, machine learning, AI-enabled experiences, reporting, and customer-facing product features.

• Improve the platform experience so engineers can add, test, deploy, and monitor new data models and workflows efficiently.

• Apply strong DataOps and software-engineering practices, including CI/CD, automated testing, code review, data-quality checks, documentation, and observability.

• Take ownership of platform reliability, including troubleshooting, incident response, Snowflake performance tuning, and cost optimization.

• Work closely with data scientists and product teams to bring machine-learning outputs and AI-enabled capabilities into production.

• Partner with software engineers to define dependable data interfaces, integration patterns, APIs, and data contracts.

• Contribute to technical and architectural decisions that improve scalability, maintainability, and access to trusted data.

• Collaborate with reporting and analytics specialists to ensure reliable data is available for Tableau and future analytics experiences.

• Participate in a shared on-call rotation, currently approximately one week out of every four, to support production platform reliability.

• Take ownership of new technical capabilities and help share knowledge across the engineering team.

What We’re Looking For

• Strong hands-on experience designing, building, deploying, and supporting production data pipelines.

• Deep production experience with at least two of the following technologies:

  • Snowflake

  • dbt

  • Airflow

• Strong Python skills and experience writing reliable, maintainable production data-processing code.

• Experience working with high-volume data, complex source systems, and cloud-based data platforms.

• Strong software-engineering fundamentals, including Git, CI/CD, automated testing, code reviews, clean-code practices, and technical documentation.

• Experience taking technical work from initial design through implementation, deployment, and ongoing production support.

• Experience diagnosing data-quality issues, failed pipelines, performance problems, and other production incidents.

• Ability to work independently, assess implementation options, and take ownership of technical decisions.

• Strong communication and collaboration skills, with the ability to work effectively across engineering, product, analytics, and data-science teams.

Nice To Have

• Experience with dbt Fusion.

• Experience with Azure, Kubernetes, or infrastructure as code.

• Familiarity with Salesforce, CRM integrations, university systems, ticketing platforms, or customer-data environments.

• Experience working with athletics, sports, fan-engagement, fundraising, advancement, or nonprofit data.

• Experience supporting machine-learning workflows, predictive models, or generative AI product capabilities.

• Familiarity with Tableau or other modern analytics and business-intelligence platforms.

• Experience with real-time or near-real-time data technologies such as Kafka or Azure Event Hubs.

• Experience with data-quality, observability, lineage, or cataloguing tools.

• Experience leading technical implementations, mentoring engineers, or introducing new engineering practices.

• A strong interest in modern software design, clean code, performance, and maintainable architecture.

• An interest in collegiate athletics, sports technology, or ticketing data.

Experience

• Seven or more years of hands-on experience in data engineering, data-platform engineering, or closely related software-engineering roles.

• Demonstrated experience building and supporting production data pipelines using modern data-engineering tools.

• Experience owning complex technical work from design through production.

Equivalent combinations of experience, technical depth, and demonstrated impact will be considered.

Education

Bachelor’s degree in Computer Science, Information Systems, Software Engineering, Engineering, or a related technical field. Equivalent practical experience will also be considered.

Compensation

The annual base salary range for this position is CAD $150,000–$185,000, based on experience, technical depth, market benchmarks, and role complexity.

We aim to provide fair and competitive compensation that reflects both the skills required for the role and the value each person brings to Kindsight.

This advertised position is for an existing vacancy at Kindsight.

At Kindsight, we’re proud to be a place where everyone belongs and has an equal opportunity to contribute, thrive, and grow. We hire based on skills, potential, and impact, and we believe our differences fuel innovation.

We welcome all individuals and do not discriminate on the basis of gender identity or expression, race, ethnicity, disability, sexual orientation, colour, religion, creed, national origin, age, marital status, pregnancy, sex, citizenship, education, languages spoken, or veteran status.

We’re building a workplace where everyone has the opportunity to do meaningful work and make a difference.

We leverage artificial intelligence tools to support certain aspects of our recruitment process. These tools may assist with résumé screening, drafting job descriptions, creating interview questions, and occasionally identifying potential candidates.

All hiring decisions are made by people, not AI. Our goal is to use AI thoughtfully to streamline administrative work, improve the candidate experience, and support fair, consistent, and unbiased hiring practices.