AI Data Enablement Engineer
New
X
Xenon7Finance data
India. Serbia. Croatia. Spain. ItalyFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 5+ years hands-on data engineering on cloud data platforms
- Required Skills
- PythonSQLAirflowdbtDatabricksPySpark
Requirements
- Have 5+ years of hands-on data engineering experience on cloud data platforms.
- Have delivered Databricks in real projects.
- Have direct hands-on experience building, configuring, and tuning Databricks Genie in production or advanced pilots, including Genie spaces with semantic models.
- Have built governed datasets with KPI definitions, hierarchies, and business glossary alignment.
- Be strong in dbt, PySpark, SQL, and Python.
- Have experience with Airflow, Databricks Workflows, or equivalent orchestration.
- Have data governance experience in regulated environments, including RBAC, RLS, masking, lineage, and auditability.
- Have experience integrating structured and unstructured data, such as PDFs, SharePoint/Teams content, and enterprise knowledge sources, into AI-enablement workflows.
- Pharma, life sciences, or regulated financial services experience is a nice to have.
- Veeva CRM, IQVIA, SAP, or clinical data source integration experience is a nice to have.
- Streamlit or Databricks Apps experience, Databricks Data Engineer Professional certification, RAG frameworks, and cost optimization experience are nice to have.
Responsibilities
- Design and build AI-ready Databricks data products with business semantics, KPIs, hierarchies, and glossary alignment.
- Implement semantic layers and governed datasets for BI and natural-language querying.
- Deploy and operate 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.
- Develop Streamlit or Databricks Apps that let business users query data without writing SQL.
- Engineer ETL/ELT pipelines using dbt, Airflow, and PySpark.
- Implement governance controls including access security, masking, lineage, auditability, catalog, and metadata management.
- Optimize data platform and AI costs and performance, including warehouse sizing, cluster tuning, query optimization, token usage, caching, and model routing.
- Partner with Finance stakeholders to translate domain requirements into semantic models and governed data products.
View Full Description & ApplyYou'll be redirected to the employer's site
