Senior / Staff Data Engineer, AI & Data Platforms
New
J
JobgetherData platforms
Fully remote opportunity for candidates based in CanadaFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- At least 5 years of professional engineering experience
- Required Skills
- AWSApache AirflowSnowflakeSparkdbtDatabricksLLMDistributed Systems
Requirements
- At least 5 years of professional engineering experience; demonstrated achievements and technical ownership are valued.
- Experience building data infrastructure, platforms, or data products from scratch.
- Strong foundations in distributed systems, platform engineering, software engineering, or modern data engineering.
- Hands-on experience with AWS or an equivalent cloud platform.
- Familiarity with Snowflake, Databricks, Spark, Airflow, and dbt.
- Experience designing streaming or event-driven architectures and building scalable, reliable production systems.
- Experience owning production environments, including monitoring, observability, troubleshooting, incident response, and on-call responsibilities.
- AI and large language model experience in data platforms, such as AI-assisted analytics, semantic layers, AI data assistants, or natural-language interfaces, is highly desirable.
- Experience developing self-service analytics, embedded analytics, or customer-facing data products.
- A track record of measurable technical or commercial impact.
- Staff-level candidates should be able to lead architecture decisions, influence technical direction, establish engineering practices, and mentor others.
- Strong product thinking, commercial awareness, analytical problem-solving, and the ability to explain and justify complex technical decisions.
Responsibilities
- Design, build, and evolve cloud-based data infrastructure and scalable data platforms.
- Develop reliable data pipelines, distributed systems, and event-driven architectures.
- Build AI-enabled data capabilities, including LLM-powered workflows, agentic data solutions, semantic layers, and natural-language analytics.
- Develop self-service analytics platforms and customer-facing or embedded data products.
- Address data streaming, governance, metadata management, and knowledge-system challenges.
- Own deployment, production reliability, observability, performance monitoring, incident response, and on-call support.
- Define architectural direction, establish engineering standards, and make technical decisions for long-term scalability.
- Collaborate with technical leadership and cross-functional teams to prioritize solutions and connect engineering investments to business outcomes.
- Mentor engineers and help establish and grow the data engineering function.
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