Senior Data Engineer, AI & Agents
Toronto
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time · Contract — employment source
Employment type in the employer’s posting: Full-Time / Contract
From the employer’s posting
What you’ll work on
Full postingBuild and register domain data agents over governed tables on Databricks and Snowflake
Migrate lakehouse source tables to open formats such as Apache Iceberg
Generate and curate catalogue metadata for automation and data-contract workflows
Work with business stakeholders and experts through iterative build, test, and validation cycles
From the employer’s posting
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.
What you’ll bring
All qualificationsCore experience
- 5+ years of professional data engineering experience
- Hands-on fluency in both Databricks and Snowflake
- Experience building data agents or query interfaces over governed datasets
- Advanced SQL with Spark / PySpark and pipeline orchestration tools
- Data quality, observability, lineage, and masking or row/column-level security experience
Preferred experience
- Databricks and Snowflake preferred
- 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).
Qualification wording
5+ years of professional experience in data engineering
genuine, hands-on fluency across both Databricks and Snowflake
Demonstrated experience building data agents or query interfaces over governed datasets
Advanced SQL together with Spark / PySpark, and experience with pipeline orchestration
Experience implementing data quality, observability, and lineage, and applying governance controls such as masking and row- and column-level security.
5+ years of professional experience in data engineering, with significant hands-on experience across modern data warehousing and lakehouse platforms (Databricks and Snowflake preferred).
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).
Education & alternatives
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).
Tools in this posting
- Python
- SQL
- AWS
- Databricks
- dbt
- Iceberg
- S3
- Snowflake
- Spark
- Airflow
- PySpark
Source — Tool mentions in context
- AI and agents: MCP; vector databases and embeddings. - Foundations: Python; YAML data contracts; Apache Iceberg; Git and CI/CD. Who you are
- 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.
- 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.
- 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.
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
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.
- 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.
- 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).
- 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.
- 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).
About Appnovation Technologies
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.
In the employer’s words · Read in context
Posting record
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- Live
- First seen by us
- Sep 4, 2026 · 55 days ago
- Recorded sightings
- 42
- Last seen by us
- Oct 29, 2026
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Job description
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.
- Governance-Minded: You understand the critical nature of data security in regulated domains and proactively apply masking and row- and column-level controls.
- Forward-Thinking: You are interested in the “big picture” of lakehouse architecture and open formats, eager to advance agent-based access patterns and best practices.
What Appnovation Offers
- Challenging and rewarding work with real impact
- Direct Access to Cutting-Edge AI Platforms
- Diverse and Inclusive Culture
- Growth opportunities for personal and professional development
- A collaborative and innovative work environment where your ideas are valued
- Exposure to exciting projects and high-profile clients
- Supportive work environment with access to company leaders
- Hybrid working model
Thank you for your interest in a career with Appnovation Technologies! Please note that only those selected for an interview will be contacted.
At Appnovation, we recognize that diverse teams are the strongest teams. Diversity, Equity & Inclusion is not only something that we embrace - we celebrate it! We are proud to be an Equal Opportunity Employer and we encourage applicants from all backgrounds, lived experiences and industries to apply. Come join us at Appnovation, and learn more about how we stay true to our company values as we build better lives through better digital.
Accommodations are available upon request throughout the recruitment process.
Accommodations are available upon request throughout the recruitment process.
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- Pay
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- Location & working pattern
Toronto
- Supportive work environment with access to company leaders - Hybrid working model Thank you for your interest in a career with Appnovation Technologies! Please note that only those selected for an interview will be contacted.
- Work authorization
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