Senior Data Engineer, AI & Agents

• 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., * 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

• 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.