# Senior Data Engineer, AI & Agents at Appnovation Technologies

> Canada · Senior · Data engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Aappnovation%3A8747541002 (read 2026-11-09).

## Facts (observed)

- Company: [Appnovation Technologies](https://alderhq.app/companies/appnovation-technologies)
- Location text: Toronto; São Paulo; Toronto
- Where it can be done from (inferred): Canada (evidence: "Location: Toronto; São Paulo; Toronto")
- Posted: 2026-09-04 (66 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-06
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/appnovation/jobs/8747541002
- Skills listed (inferred): Python, AWS, Spark, Airflow, Snowflake, dbt, SQL

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Toronto, n 11): p25 CAD 131k, median CAD 135k, p75 CAD 160k

## Description (the employer's text)

### About us

Appnovation is the AI Product Company. Headquartered in Vancouver, Canada, with teams around the world, Appnovation builds AI products, delivers enterprise AI and digital solutions, and co-invests with clients to bring new AI products to market.

Open source has been the foundation of Appnovation's work since 2007, and it remains the foundation underneath every AI product the company builds today. Appnovation teams work with organizations in highly regulated industries, including global leaders such as Pfizer, Google and AstraZeneca, to help them adopt AI they can govern and own.

As a Senior Data Engineer, AI & Agents, you will prepare enterprise domain data for consumption by AI agents, partnering directly with business stakeholders and subject-matter experts. The role centers on building and registering domain-grounded data agents over governed datasets, performing lakehouse migrations, and onboarding new data domains — including commercial, finance, research and development, and real-world data — onto the enterprise data platform. You will operate across Databricks and Snowflake, converting source tables to open formats and generating the catalogue metadata that powers downstream automation and data-contract workflows, all while ensuring high data quality, governed access, and reliable agent-based access to trusted data. The ideal candidate brings deep, hands-on data engineering expertise, strong governance instincts, and excellent stakeholder-facing skills.

### Key Responsibilities

- Build and register domain data agents at scale over governed tables across Databricks and Snowflake.
- Perform lakehouse migrations, including converting source tables to open formats (e.g., Apache Iceberg) to enable agent-based access.
- Generate and curate catalogue metadata that feeds downstream automation and data-contract workflows.
- Partner with business stakeholders and subject-matter experts through iterative build, test, and validation cycles.
### Qualifications

- Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field.
- 5+ years of professional experience in data engineering, with significant hands-on experience across modern data warehousing and lakehouse platforms (Databricks and Snowflake preferred).
- Strong data engineering background with genuine, hands-on fluency across both Databricks and Snowflake.
- Demonstrated experience building data agents or query interfaces over governed datasets (e.g., Snowflake Cortex or Genie).
- Advanced SQL together with Spark / PySpark, and experience with pipeline orchestration (dbt, Apache Airflow, or Databricks Workflows).
- Experience implementing data quality, observability, and lineage, and applying governance controls such as masking and row- and column-level security.
- Excellent stakeholder-facing skills, with a track record of translating business requirements into delivered data assets.
### Preferred Skills

- Familiarity with MCP-based data exposure and with embeddings or vector search for retrieval-augmented use cases.
- Experience with AWS and S3, in anticipation of onboarding native cloud data sources.
- Experience with regulated life-sciences data domains (clinical, commercial, or real-world data).
### Technical Experience

- Data platforms: Databricks, Snowflake (Cortex, Genie); AWS and S3.
- Pipelines and modelling: SQL, PySpark, dbt, Airflow / Databricks Workflows.
- Governance and catalogue: Unity Catalogue, Horizon, Collibra.
- AI and agents: MCP; vector databases and embeddings.
- Foundations: Python; YAML data contracts; Apache Iceberg; Git and CI/CD.
### Who you are

- Agent-Oriented Builder: You enjoy turning governed datasets into reliable, domain-grounded agents that business users can query with confidence.
- Quality-Focused: You are rigorous about data accuracy, lineage, and observability, ensuring high standards through validation before data reaches agents or the business.
- Collaborative Partner: You thrive working directly with stakeholders and subject-matter experts through iterative build, test, and validation cycles.

---
Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aappnovation%3A8747541002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.