Senior Data Engineer (AI & Agents)

Join our team as a Senior Data Engineer, where you will play a crucial role in preparing enterprise domain data for AI agents. You will work closely with business stakeholders and subject-matter experts to build and register domain-grounded data agents, perform lakehouse migrations, and onboard new data domains onto the enterprise data platform. Your expertise in data engineering, strong governance instincts, and excellent stakeholder-facing skills will be essential in ensuring high data quality, governed access, and reliable agent-based access to trusted data.

About Appnovation

Collaborative Partner: You thrive working directly with stakeholders and subject-matter experts through iterative build, test, and validation cycles

Responsibilities

• Construire et enregistrer des agents de données de domaine à grande échelle sur des tables régulées dans Databricks et Snowflake.
• Effectuer des migrations de lac de données, y compris la conversion de tables sources en formats ouverts (par exemple, Apache Iceberg).
• Générer et organiser des métadonnées de catalogue qui alimentent l'automatisation en aval et les flux de travail de contrat de données.
• The ideal candidate brings deep, hands-on data engineering expertise, strong governance instincts, and excellent stakeholder-facing skills
• Governance and catalogue: Unity Catalogue, Horizon, Collibra

Requirements

• 5+ years of professional experience in data engineering, with significant hands-on experience across modern data warehousing and lakehouse platforms (Databricks and Snowflake preferred)
• Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field
• 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)
• 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
• Advanced SQL together with Spark / PySpark, and experience with pipeline orchestration (dbt, Apache Airflow, or Databricks Workflows)
• Foundations: Python; YAML data contracts; Apache Iceberg; Git and CI/CD

AI and agents: MCP; vector databases and embeddings

• Data platforms: Databricks, Snowflake (Cortex, Genie); AWS and S3
• Pipelines and modelling: SQL, PySpark, dbt, Airflow / Databricks Workflows

Technical Experience

• 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
• 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
• Governance-Minded: You understand the critical nature of data security in regulated domains and proactively apply masking and row- and column-level controls

Who you are

• 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)

Preferred

• Familiarity with MCP-based data exposure and with embeddings or vector search for retrieval-augmented use cases