Databricks Genie AI Engineer
DysrupIT (company site, opens in a new tab)BengaluruRemoteDirect career pageEst. 12–20 LPA
Posted 16h ago
Reach people at DysrupIT
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at DysrupIT you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own the semantic layer and Databricks Genie Spaces that turn business questions into governed, reliable analytics.
- Define KPI logic and build evaluation and regression tests; diagnose inaccurate answers, SQL, joins, or metric interpretation and improve the underlying metadata or models.
- Partner with data, platform, and business teams on upstream data quality and the deployment, versioning, and monitoring of semantic assets.
Minimum Requirements
- Work Experience Required: Not explicitly mentioned in the JD.
- Hands-on Databricks experience, including Databricks SQL, Unity Catalog, and Genie; strong SQL plus proficiency in Python and/or Scala.
- Experience in semantic modelling or metric layers, data warehousing and dimensional modelling, and testing or validation frameworks for analytics or AI systems.
- Familiarity with LLMs, prompt engineering, or RAG, and experience with at least one cloud platform: AWS, Azure, or GCP.
Ideal Candidate Profile
- Has practical Databricks experience and can configure Genie while shaping business-friendly, governed definitions for tables, metrics, and KPIs.
- Can systematically evaluate natural-language analytics for accuracy and trace failures back to data models, metadata, instructions, or configuration.
- Fits a cross-functional environment, translating business feedback into concrete changes alongside data engineering, platform, and DevOps teams.