Lead AI and Data Engineer

Dutch; Vancouver; On-site; CAD 120,000 - 180,000; Full time

Job summary

Dutch is seeking a hands-on Lead AI/Data Engineer in Vancouver to design and ship production AI agents and their underlying data infrastructure. You will own the data pipelines, warehouse models, analytics, and experimentation infrastructure to support AI features and product analytics.

You’ll build reliable ELT pipelines, manage cost and observability, and collaborate across product and engineering teams to bring capabilities into production.

Qualifications

• Hands-on experience with AI agents and LLM APIs.
• Experience deploying and operating services on cloud with CI/CD.
• Strong Python and SQL for production systems.

Responsibilities

• Build and scale production AI agents and services for users and internal teams.
• Own AI development infrastructure, including observability and cost management.
• Design, run and evaluate experiments across models, prompts, and retrieval strategies.
• Develop data pipelines and analytics infrastructure to support AI features and experiments.
• Maintain data quality tests, metrics, and dashboards for product analytics.

Skills

LLM APIs

Agent frameworks

Tool calling

Retrieval

Experimental design

Python

SQL

Cross-functional

Data platforms

Tools

Snowflake

dbt

Prefect

Airflow

Dagster

Fivetran

Airbyte

Job description

• We’re looking for a hands-on Lead AI/Data Engineer to build and scale the AI systems powering Dutch. You’ll design and ship production AI agents and services for pet parents, veterinarians, and internal teams. You’ll also own the underlying AI infrastructure, including evaluation frameworks, prompt optimization, observability, and cost management
• You’ll help shape our technical approach to AI development, establish engineering standards, and work closely with product and engineering teams to bring new capabilities into production. This role also includes ownership of key parts of our data platform. You’ll build and maintain data pipelines, warehouse models, and analytics and experimentation infrastructure that support our AI systems and broader business needs
• Design, build, and operate production AI agents and services for pet parents, veterinarians, and internal teams, grounded in our data and built with appropriate safeguards
• Own the AI development infrastructure, including evaluation frameworks, prompt optimization, LLM observability, and cost management
• Establish engineering standards and reusable patterns for retrieval, tool calling, guardrails, and model selection
• Design and run systematic experiments across models, prompts, retrieval strategies, and agent architectures. Build evaluation datasets and automated tests to measure quality, safety, performance, and cost, and detect regressions
• Evolve the semantic layer that enables AI systems to answer questions about our business data accurately
• Work closely with product engineering to deliver AI capabilities through APIs and integrate them into production applications
• Build and maintain reliable ELT pipelines and dbt models in Snowflake, using Prefect for orchestration and Fivetran and Airbyte for data ingestion
• Develop trusted data marts and datasets that support AI features, product analytics, and experimentation
• Build the data foundation for experimentation, including consistent metrics, baselines, and measurement of feature impact
• Maintain automated data quality tests, freshness checks, and alerts for critical pipelines
• Diagnose pipeline failures, address root causes, and improve reliability
• Support warehouse security, governance, performance, and cost management
• Build and maintain reliable event data pipelines from Segment into Amplitude, Iterable, and Snowflake
• Partner with product engineering to define and validate event tracking plans for new features, ensuring accurate and consistent instrumentation
• Diagnose and resolve identity resolution, user property, and event tracking issues that affect AI systems and product analytics
• Support product experimentation through reliable event tracking, consistent metrics, and accurate measurement of experiment outcomes
• Build and maintain Amplitude dashboards and reports that help product teams understand user behavior and measure feature performance

• Hands-on experience developing AI agents and applications using LLM APIs, agent frameworks, tool calling, retrieval, and structured outputsYou use evaluations, experiments, and data to guide decisions and continuously improve system performanceYou thrive in a small team, set priorities independently, and make progress with limited directionYou take ownership of technical decisions, from architecture and implementation through deployment and ongoing operationsFamiliarity with BI tools such as Sigma, Looker, or TableauYou work effectively across data, product, and engineering teams, and understand how to integrate AI capabilities into existing applicationsExperience deploying and operating services on AWS, GCP, or Azure, using containers and CI/CD pipelinesFamiliarity with product analytics and event tracking platforms such as Segment and Amplitude, or a willingness to learnExperience designing and running LLM evaluations, including test datasets, automated graders, regression testing, and systematic experimentationYou’re comfortable working across AI and data engineering, including pipelines, data models, and analytics infrastructureYou love animalsWorking knowledge of modern data platforms, including Snowflake or similar warehouses, dbt, and orchestration tools such as Prefect, Airflow, or DagsterExperience with LLM observability, performance optimization, and cost management in productionYou build reusable tools, frameworks, and infrastructure that help teams develop and ship AI capabilities fasterYou’ve built and operated production AI systems that people rely on, and you know when AI is the right solution to a problemStrong Python and SQL skills, with experience building reliable, maintainable production systemsYou communicate technical decisions clearly and document your work so others can build on it6+ years of experience in software, data, or ML engineering, including 2+ years building and operating LLM applications in productionExperience with prompt optimization, semantic layers, data contracts, or A/B testing and experimentation infrastructure