Senior Data Engineer (AI & Agents)

Join our team as a Senior Data Engineer, AI & Agents. In this role, you will prepare enterprise domain data for AI agents, build and register domain-grounded data agents, perform lakehouse migrations, and onboard new data domains onto the enterprise data platform. You will work across Databricks and Snowflake, ensuring high data quality, governed access, and reliable agent-based access to trusted data. The ideal candidate will have 5+ years of professional experience in data engineering, strong governance instincts, and excellent stakeholder-facing skills.

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 à travers Databricks et Snowflake.
• Effectuer des migrations de lac de données, y compris la conversion des tables sources en formats ouverts (par exemple, Apache Iceberg) pour permettre l'accès basé sur des agents.
• Générer et organiser les métadonnées du catalogue qui alimentent l'automatisation en aval et les flux de travail des contrats de données.
• Advanced SQL together with Spark / PySpark, and experience with pipeline orchestration (dbt, Apache Airflow, or Databricks Workflows)
• Governance and catalogue: Unity Catalogue, Horizon, Collibra
• Foundations: Python; YAML data contracts; Apache Iceberg; Git and CI/CD

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)
• Strong data engineering background with genuine, hands-on fluency across both Databricks and Snowflake
• Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field
• 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
• Pipelines and modelling: SQL, PySpark, dbt, Airflow / Databricks Workflows

AI and agents: MCP; vector databases and embeddings

• Data platforms: Databricks, Snowflake (Cortex, Genie); AWS and S3
• Quality-Focused: You are rigorous about data accuracy, lineage, and observability, ensuring high standards through validation before data reaches agents or the business
• Agent-Oriented Builder: You enjoy turning governed datasets into reliable, domain-grounded agents that business users can query with confidence
• 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
• 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