Staff Backend Engineer (Data & AI)

Location: Toronto, Ontario

Our client is on a mission to power the future of customer support through the responsible and efficient deployment of GenAI bots in contact centers. Our platform transforms raw enterprise data into a secure, structured, and model-ready format, enabling companies to deploy powerful AI assistants with confidence.

They help organizations avoid the costly mistakes of feeding unclean data to LLMs — ensuring their bots don’t hallucinate or mislead customers.

The Role:

We’re looking for a Staff/Data Engineer to join as a key engineering hire. You’ll work closely with the VP of Engineering & AI to build the foundation of our data platform, shaping everything from ingestion to transformation to pipeline orchestration.

This is a hands-on, high-impact role — perfect for someone who thrives in early-stage environments and wants to take full ownership of backend/data systems from the ground up.

What You’ll Do:

1. Architect, build, and maintain robust and scalable data pipelines to support GenAI applications.
2. Own data ingestion and transformation processes, integrating data from multiple structured and unstructured sources.
3. Design and implement Retrieval-Augmented Generation (RAG) systems.
4. Work with vector databases, embeddings, and LLM-adjacent infrastructure.
5. Collaborate with AI/ML engineers to ensure data is clean, structured, and optimized for model consumption.
6. Serve as the go-to technical expert for all things data — pipelines, storage, security, and performance.
7. Establish and enforce best practices for data engineering across the org.

Special Perks:

- Join a technology startup led by a 7x founder that has had successful exits.
- Be part of the foundational team and build products that solve massive problems and have huge upside.
- Have some skin in the game with a very aggressive equity component (along with strong base salary).

Must Have Skills:

What We’re Looking For:

1. 6+ years of backend/data engineering experience, ideally in high-growth tech environments.
2. Deep expertise in building and managing data pipelines (Airflow, Dagster, dbt, etc.).
3. Strong Python skills (Pandas, FastAPI, PySpark).
4. Experience with cloud data warehouses (Snowflake, BigQuery, Redshift) and storage systems (S3, GCS).
5. Proficient with SQL and schema design.
6. Hands-on experience with vector databases (e.g., Pinecone, Weaviate, FAISS).
7. Familiarity with LLMs, embeddings, and building RAG pipelines.
8. Strong understanding of data security and compliance best practices.

Nice to Have Skills:

Preferred Attributes:

1. Experience in early-stage startups (0•1 product experience a big plus).
2. Highly autonomous and comfortable in ambiguous environments.
3. Excellent communication skills — able to work cross-functionally with product, design, and AI/ML.
4. Proven ability to simplify complex data systems and make technical decisions with clarity.
5. Experience building AWS data pipelines.