Senior Data Engineer

This is a fully remote position, open to applicants in Canada.

📋 Description

• Take ownership of the complete design, delivery, and operation of significant data products, including high-throughput ETL/ELT pipelines, transformation workflows, and storage solutions.

• Collaborate with engineering, product, and business stakeholders to clarify requirements, challenge assumptions, define practical solutions, and drive projects from design to production launch.

• Tackle challenging data engineering issues by creating performance-optimized transformations and services using Python, high-performance SQL, and tools such as dbt.

• Make informed technical trade-offs at the product level, balancing scalability, reliability, delivery speed, maintainability, and cost.

• Produce and lead technical designs and RFCs that effectively communicate architecture, alternatives, risks, and operational considerations.

• Shape the technical direction of the data products you oversee and contribute to the wider technical strategy and roadmap of the team.

• Maintain a high quality standard through careful design and code reviews, automated testing, version-controlled schemas, and CI/CD practices for data pipelines.

• Direct projects that enhance the team’s engineering and operational excellence, focusing on data observability, lineage, incident response, deployment safety, and developer productivity.

• Design and manage scalable batch and real-time data solutions utilizing technologies such as Spark, Flink, and Kafka.

• Collaborate with the ML Platform team to provide clean, reliable, and feature-rich datasets for model training and inference.

• Promote data stewardship through comprehensive documentation, discoverability, privacy controls, access management, and governance practices.

• Conduct postmortems for data incidents, identify systemic improvements, and ensure the completion of corrective actions.

• Decompose complex projects into clear, manageable tasks and assist in coordinating execution among team engineers.

• Mentor other engineers, offer actionable technical feedback, and engage in the interviewing and hiring process.

• Participate in the team's on-call rotation, addressing incidents, troubleshooting issues, and contributing to root cause analysis and service reliability enhancements.

• Respond to and resolve production issues, outages, and critical alerts during assigned on-call shifts.

• Escalate incidents as necessary and work with team members to restore services.

• Document issues and contribute to initiatives aimed at reducing recurring incidents.

• Be available for occasional after-hours, weekend, and holiday support while on call.

⛳️ Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field, or equivalent practical experience.

• Over 5 years of relevant industry experience in data engineering, distributed systems, or a similar field.

• Proven track record of independently delivering and managing significant production data products end to end.

• Experience with AI-assisted development tools for coding, debugging, testing, documentation, technical exploration, and analysis.

• Ability to assess AI-assisted outputs, identifying risks and failure modes, ensuring protection of sensitive information, and recognizing when deeper manual review is necessary.

• Extensive experience in building and scaling cloud-based data platforms, ideally using AWS and Snowflake.

• Proficiency in developing, optimizing, and debugging complex data transformations with Python and high-performance SQL.

• Strong understanding of data modeling, ETL/ELT architecture, schema design, and transformation frameworks such as dbt.

• Experience in designing reliable workflows with orchestration tools like Airflow.

• Hands-on experience with distributed processing or streaming technologies such as Spark, Flink, or Kafka.

• Strong technical acumen with experience in making trade-offs among performance, reliability, scalability, maintainability, and cost.

• Experience in establishing or enhancing data quality, testing, observability, lineage, and operational practices.

• Familiarity with modern data governance practices, including cataloging, data classification, PII masking, privacy controls, and access management.

• Experience in drafting technical design documents and conveying complex decisions to both technical and non-technical stakeholders.

• Familiarity with technologies such as Iceberg, Debezium, or infrastructure-as-code tools like Terraform is advantageous.

• Ability to participate in a shared on-call rotation for production systems, including occasional after-hours, weekend, and holiday coverage, with responsibilities including responding to critical alerts, coordinating escalations to restore service, and documenting issues to aid in preventing recurrence.

🏝️ Benefits

• Equity offerings

• Bonus opportunities