This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer based in Canada.
This is an opportunity to build and own the data infrastructure that powers critical business decisions, reporting, operations, internal tools, and AI initiatives. You’ll take complex business needs and turn them into reliable, scalable data products from source systems through production. The role combines hands-on engineering with end-to-end ownership of pipelines, data quality, and production operations. You’ll work across Python, SQL, Airflow, dbt, APIs, and modern cloud data infrastructure. You’ll collaborate closely with Finance, Operations, Growth, and Technology teams in a fast-moving environment where priorities evolve quickly. Success means creating trusted systems that are resilient, well-documented, and increasingly efficient through thoughtful use of AI-powered development tools.
Accountabilities
- Design, build, and own custom data pipelines that integrate source systems with the data warehouse, including complex or poorly documented APIs.
- Develop and operate Airflow DAGs for scheduled and event-driven workflows, ensuring reliable and scalable data processing.
- Build dbt models and governed semantic layers that provide trusted data for analysts, internal applications, and AI tools.
- Create reusable patterns for incremental synchronization, retries, replay, backfills, and event processing to improve engineering efficiency.
- Implement monitoring, data-quality checks, reconciliation, alerting, and logging to identify issues before they affect reporting or downstream systems.
- Diagnose and resolve production incidents involving missing, delayed, duplicated, or inaccurate data, taking ownership through resolution.
- Partner with Finance, Operations, Growth, and Technology stakeholders to translate ambiguous business needs into durable data solutions.
- Use AI coding agents to accelerate implementation, testing, debugging, refactoring, and review while maintaining responsibility for technical quality and correctness.
- Document architectural decisions, system ownership, operational procedures, and recovery processes to ensure systems remain maintainable and supportable.
- Establish scalable engineering patterns and best practices that help the broader team deliver reliable data products faster.
Requirements
- 3+ years of experience in data engineering, analytics engineering, or a closely related engineering role, with demonstrated ownership beyond execution-focused responsibilities.
- Strong production-level Python and advanced SQL skills, with an emphasis on clean, maintainable, and well-tested code.
- Hands-on experience designing, building, and operating Airflow workflows for custom data pipelines.
- Strong knowledge of dbt and dimensional modeling, including grain, keys, facts, dimensions, incremental models, testing, documentation, and schema evolution.
- Experience developing custom integrations using third-party APIs, webhooks, files, or event streams rather than relying exclusively on managed connectors.
- Deep understanding of API ingestion, including authentication, pagination, rate limits, incremental synchronization, retries, and historical backfills.
- Experience owning business-critical pipelines throughout their lifecycle, including design, production support, monitoring, alerting, logging, reconciliation, CI/CD, and runbooks.
- Experience working in a high-growth environment where priorities, business requirements, and source systems change frequently; DTC or subscription experience is a plus.
- Broad, hands-on understanding of the modern data stack rather than specialization in only one technical area.
- Strong analytical and problem-solving abilities, with a habit of investigating discrepancies until the underlying cause is identified.
- Ability to translate business decisions and requirements into appropriate technical solutions while challenging unclear definitions or conflicting expectations.
- Strong ownership, communication, documentation, and collaboration skills, with the ability to work independently in a remote environment.
- Fluency with AI coding agents for implementation, testing, debugging, refactoring, and review, combined with the judgment to validate and correct AI-generated output.
- Experience with BigQuery, GCP, or Cloud Composer is a plus.
Benefits
- Competitive base salary of $150,000–$170,000, plus a bonus, depending on experience and location.
- Comprehensive benefits package designed to support employee and family well-being.
- Strong healthcare and benefits coverage.
- Generous paid time off.
- Wellness-focused perks and access to company products.
- Fully remote, high-trust working environment.
- Opportunities for significant ownership, impact, and career growth in a fast-scaling organization.
- Biannual company off-sites providing opportunities to connect with colleagues in person.
- Opportunity to work with modern data infrastructure and AI-powered engineering tools.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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