Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

What you’ll work on

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.
Partner with business stakeholders and subject-matter experts through iterative build, test, and validation cycles.

From the employer’s posting

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.

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.

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

What you’ll bring

All qualifications

Core experience

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
Demonstrated experience building data agents or query interfaces over governed datasets (e.g., Snowflake Cortex or Genie).
Experience implementing data quality, observability, and lineage, and applying governance controls such as masking and row- and column-level security.

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

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).

Demonstrated experience building data agents or query interfaces over governed datasets (e.g., Snowflake Cortex or Genie).

Experience implementing data quality, observability, and lineage, and applying governance controls such as masking and row- and column-level security.

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

- 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.

Appnovation is a global, full-service digital partner that combines Strategy, Experience & Design, Engineering and Managed Services. We build digital solutions that deliver real impact today and serve as foundations for future growth. Bold ambition. Practical action. Endless possibilities. 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

We build digital solutions that deliver real impact today and serve as foundations for future growth.

In the employer’s words · Read in context

Job description

View original posting ↗

About us

Appnovation is a global, full-service digital partner that combines Strategy, Experience & Design, Engineering and Managed Services. We build digital solutions that deliver real impact today and serve as foundations for future growth. Bold ambition. Practical action. Endless possibilities.

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.

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Source & posting history

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Source excerpts

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Pay: No pay amount identified in the saved description.
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: No clear work-authorization passage found. Eligibility is unconfirmed.

Open source at job-boards.greenhouse.io ↗Read the full description ↓

Status in our records: Active
First seen by us: Sep 4, 2026
Recorded sightings: 20
Last seen by us: Oct 8, 2026

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