Senior Data Engineer

Small Door Veterinary•Montreal, QC

57d

About The Position

Small Door is membership-based veterinary care designed with human standards that is better for pets, pet parents, and veterinarians alike. We designed and delivered a reimagined veterinary experience via a membership that includes exceptional care, 24/7 telemedicine, and transparent pricing - delivered with modern hospitality in spaces designed by animal experts to be stress-free. We opened our flagship location in Manhattan's West Village in 2019 and have quickly expanded across the East Coast. Small Door now operates in New York City, Boston, Washington DC, Maryland and Virginia with continued expansion plans in 2026. The Senior Data Engineer will report directly to the VP of Data and Technology and will serve as a technical leader and mentor within the data team. This role is based in Montreal. We are seeking a Senior Data Engineer with a Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (Master’s preferred), coupled with 5+ years of hands-on experience building and scaling data pipelines. The ideal candidate will bring deep expertise in ETL/ELT workflows, data warehousing, and production-grade pipelines that support machine learning models and retrieval-augmented generation (RAG) systems. Beyond technical depth, you will mentor and guide a junior data engineer, help organize and prioritize the team’s workload, and champion best practices across the data engineering function. Soft skills are paramount in this role. We’re looking for someone with excellent communication skills, both verbal and written, who can translate complex technical concepts for non-technical stakeholders and coach less experienced team members. You should be proficient in English, as clients that we support are based outside of Quebec, and will require communication in English. Additionally, strong problem-solving skills, sound judgment, and a proactive mindset are essential for driving technical decisions and resolving issues across our data stack.

Requirements

5+ years of relevant experience as a Data Engineer or in a similar data-focused role. Experience in startups and fast-paced environments strongly preferred. A Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field is required; a Master’s degree is a plus.
Strong proficiency in Python and SQL for data manipulation, pipeline development, and scripting at scale
Deep experience with at least one orchestration framework (e.g., Airflow, Dagster, Prefect) for scheduling and managing complex data workflows
Solid experience with cloud platforms (AWS preferred) and their data services (e.g., S3, RDS, Redshift, Lambda, Glue)
Hands-on experience with ML pipeline concepts: feature stores, model training data preparation, batch/streaming inference pipelines
Working knowledge of RAG pipeline components: document loaders, text chunking strategies, embedding models, and vector databases (e.g., Pinecone, Weaviate, pgvector, Chroma)
Strong experience with Snowflake and data warehousing tools (e.g., dbt, BigQuery). Experience with data lake architectures is a plus.
Proficiency with AI-assisted engineering practices, including the use of agentic coding tools (e.g., Claude Code, GitHub Copilot, Cursor) to accelerate development workflows, code generation, and debugging
Deep understanding of data modeling, schema design, and data quality frameworks
Demonstrated ability to mentor junior engineers, lead technical discussions, and influence technical direction without formal authority
Strong organizational skills with a track record of prioritizing competing workstreams and delivering projects on time
Experience with version control (Git), CI/CD pipelines, and infrastructure-as-code practices

Responsibilities

Own the design, development, and reliability of production data pipelines using Python, SQL, and modern orchestration tools
Architect and implement ETL/ELT workflows to move data between source systems, data warehouses, and downstream consumers at scale
Lead the development and optimization of data pipelines that feed machine learning models, including feature engineering, training data preparation, and inference pipelines
Design and maintain ingestion and chunking pipelines for RAG systems, including document parsing, embedding generation, and vector store population
Mentor and guide a more junior data engineer: conduct code reviews, pair on complex problems, and foster their technical growth
Organize and prioritize data engineering tasks, ensuring the team delivers reliably against business timelines and technical standards
Collaborate with data scientists, ML engineers, and product teams to productionize models and ensure reliable, performant data delivery
Design and implement monitoring, alerting, and data quality frameworks to proactively catch pipeline failures and data drift
Drive the design and evolution of our data warehouse and data lake architecture, making strategic decisions about tooling, partitioning, and performance
Work with stakeholders across the business to understand data needs and translate them into scalable, well-documented data solutions
Champion engineering best practices: documentation, testing, CI/CD, and knowledge sharing within the team

Benefits

Competitive salary
Equity ownership
Health, dental + vision insurance
Upward mobility and growth opportunities
Generous paid-time off, parental leave, and company wide holidays
Discounted veterinary care for your loved ones
Growth opportunities
An opportunity to make a real impact on the people around you
A collaborative group of people who live our core values and have your back
Small Door is proudly committed to creating a diverse, inclusive and equitable workplace. We encourage qualified applicants of every background, ability, and life experience to apply to appropriate employment opportunities.