AI Data Enablement Engineer

Xenon Seven

. Design and build AI-ready data products on Snowflake and/or Databricks .

Posted 9/18/2026full-timeBarcelona • SpainMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building AI-ready data products using Snowflake and Databricks, with a strong focus on data governance, ETL/ELT pipeline engineering, and semantic layer implementation in regulated environments. Proficient in optimizing data platform performance and cost while ensuring business relevance and user accessibility.

Highest-signal resume keywords

Snowflake Data EngineeringDatabricks Data EngineeringETL/ELT Pipeline EngineeringData Governance in Regulated EnvironmentsSemantic Layer Implementation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SnowflakeDatabricksDbtPySparkSQLPythonAirflowData GovernanceData IntegrationCost Optimization

Tools & Technologies

Snowflake CortexDatabricks GenieStreamlitUnity CatalogDatabricks Workflows

Certifications & Qualifications

SnowPro AdvancedDatabricks Data Engineer Professional

Industry Keywords

PharmaLife SciencesRegulated Financial ServicesRBACRLS

Tech Stack

Tools & technologies

AirflowCloudETLPySparkPythonSQLUnity

About the role

Key responsibilities & impact

Design and build AI-ready data products on Snowflake and/or Databricks
Implement semantic layers and governed datasets for traditional BI and natural-language querying
Deploy and operate Snowflake Cortex capabilities and/or Databricks Genie spaces with Unity Catalog, tuning for accuracy, adoption, and business relevance
Build RAG pipelines and conversational analytics applications grounded in governed enterprise data
Build Streamlit or Databricks Apps that enable business users to query data without writing SQL
Engineer ETL/ELT pipelines using dbt, Airflow, Snowpark, and PySpark
Implement data governance, including RBAC, row/column-level security, masking, lineage, auditability, catalog, and metadata management, in a regulated pharma environment
Optimize data-platform cost and performance through warehouse sizing, cluster tuning, and query optimization
Optimize AI cost and performance through token usage management, caching, and model routing
Partner with Finance stakeholders to translate domain requirements into semantic models and governed data products

Requirements

What you’ll need

5+ years hands-on data engineering on cloud data platforms — Snowflake and/or Databricks demonstrated in real project delivery, not skill-list-only
Direct hands-on experience with either Snowflake Cortex or Databricks Genie, built, configured, and tuned in production or advanced pilots, with specific reference to the flavors used
Semantic layer / trusted data product delivery, including governed datasets with KPI definitions, hierarchies, and business glossary alignment
Strong proficiency across dbt, PySpark, Snowpark, SQL, and Python
Experience with orchestration using Airflow, Databricks Workflows, or equivalent
Data governance experience in regulated environments, including RBAC, RLS, masking, lineage, and auditability
Experience integrating structured and unstructured data, including PDFs, SharePoint/Teams content, and enterprise knowledge sources, into AI-enablement workflows
Nice to have: Pharma, life sciences, or regulated financial services domain experience
Nice to have: Veeva CRM, IQVIA, SAP, or clinical data source integration
Nice to have: Streamlit or Databricks Apps for business-facing analytics
Nice to have: SnowPro Advanced or Databricks Data Engineer Professional certification
Nice to have: LangChain, LlamaIndex, or equivalent RAG frameworks
Nice to have: Cost optimization across compute and LLM dimensions