About this role
Building and registering domain-grounded data agents over governed datasets takes up a large portion of the daily routine at Appnovation Technologies. The engineering group develops digital solutions across platforms like Databricks and Snowflake, focusing specifically on preparing enterprise domain data so that AI agents can consume it reliably. Team members handle lakehouse migrations, convert source tables into open formats, and curate metadata that powers downstream automation and data-contract workflows.
Created to bridge the gap between complex enterprise data and practical agent-based access, the role requires continuous collaboration with business stakeholders and subject-matter experts. Professionals who possess deep data engineering expertise, strong governance instincts, and clear stakeholder management skills tend to thrive in this environment. Success means ensuring high data quality, robust access controls, and dependable query interfaces for various commercial, financial, and research domains.
What you will do
- Build and register domain data agents at scale over governed tables across Databricks and Snowflake.
- Perform lakehouse migrations by converting source tables to open formats like 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 need
- Hold a Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field.
- Bring 5+ years of professional experience in data engineering with significant hands-on practice across modern data warehousing and lakehouse platforms.
- Demonstrate genuine, hands-on fluency across both Databricks and Snowflake.
- Show direct experience building data agents or query interfaces over governed datasets using tools such as Snowflake Cortex or Genie.
- Apply advanced SQL together with Spark, PySpark, and pipeline orchestration tools like dbt, Apache Airflow, or Databricks Workflows.
- Implement data quality, observability, lineage, and governance controls including data masking and security policies.
- Exhibit excellent stakeholder-facing skills with a track record of translating business requirements into delivered data assets.
Nice to have
- 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 such as clinical, commercial, or real-world data.
Benefits
- Challenging and rewarding work with real impact
- Growth opportunities for personal and professional development
- Supportive work environment with access to company leaders
- Hybrid working model
Skills and tech stack
Questions candidates ask
What is the working model for this role?
Appnovation offers a hybrid working model.
What platforms and technical tools will I use?
The engineering work operates across Databricks and Snowflake, utilizing advanced SQL, PySpark, dbt, Apache Airflow, or Databricks Workflows, alongside open formats like Apache Iceberg.
Who will I be working with on a regular basis?
You will partner directly with business stakeholders and subject-matter experts through iterative build, test, and validation cycles.
The application is on the official Appnovation Technologies careers page.
Apply on company site