Staff Data Engineer
New
J
JobgetherData engineering
Fully remote position for candidates based in Canada.Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years of professional data engineering experience
- Required Skills
- PythonSQLKafkaCI/CDScala
Requirements
- Have 7+ years of professional data engineering experience, including leading complex data platform initiatives.
- Bring a strong systems architecture background and deep expertise in distributed data systems.
- Demonstrate expert proficiency in Python, Scala, and SQL.
- Have extensive experience with cloud-native data platforms and enterprise data warehousing.
- Have strong expertise in pipeline orchestration, processing, transformation, and data modeling.
- Have hands-on experience with streaming and real-time processing technologies such as Kafka, Kinesis, or Pub/Sub.
- Have experience implementing data quality, governance, and compliance frameworks.
- Have experience with container orchestration and CI/CD for data systems.
- Have built production AI/ML data pipelines involving embeddings, vector stores, RAG preparation, feature stores, and training or inference workflows.
- Have demonstrated technical leadership and mentoring experience.
- Be able to explain complex technical concepts to technical and non-technical audiences.
- Have day-to-day expertise with AI-assisted coding tools such as Claude and Cursor, including their practical strengths and limitations.
- Be legally authorized to work in Canada; employment-based visa sponsorship requirements may apply.
- Be willing to travel, including approximately 20% U.S.-based travel and possible final-interview and onboarding travel.
- AWS certifications, particularly AWS Certified Data Engineer – Associate, are strongly preferred; data mesh or data fabric, lakehouse architectures, governance frameworks, and healthcare data experience are pluses.
Responsibilities
- Define data architecture and platform strategy across pipelines, warehouses, data lakes, and distributed data systems.
- Build and optimize batch and real-time data pipelines for reliability, scalability, performance, and cost efficiency.
- Establish data governance, quality, compliance, and engineering standards.
- Implement monitoring, logging, and alerting, and contribute to CI/CD workflows for data deployment and automation.
- Make platform modernization and architecture decisions that account for immediate needs and long-term tradeoffs.
- Design data contracts and event flows with backend, platform, and engineering teams.
- Build production data pipelines for AI/ML systems, including embeddings, vector stores, RAG preparation, feature stores, and training and inference flows.
- Integrate data services with APIs, middleware, and third-party systems.
- Partner with leadership and cross-functional teams on data strategy and platform objectives.
- Mentor junior and mid-level engineers and use AI-assisted development tools such as Claude and Cursor.
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