Remote Databricks Genie AI Engineer - Semantic Layer

Cognizant

Blaine (MN)

Remote

USD 88,000 - 132,000

Full time

14 days+

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Benefits offered by this job

Medical/Dental/Vision/Life Insurance

Paid holidays plus Paid Time Off

401(k) plan and contributions

Long-term/Short-term Disability

Paid Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant is seeking a Databricks Genie AI Engineer to design semantic models and govern AI-enabled analytics. You will shape the data foundation powering natural language interactions and ensure trusted insights for business users.

You will build and maintain the semantic layer, Genie Spaces, metric views, and validation frameworks while collaborating with data engineers and DevOps on deployment and observability. Strong SQL and Python/Scala skills and cloud experience are essential.

Qualifications

Experience with Databricks, including Databricks SQL, Unity Catalog, and Genie or comparable AI-powered analytics.
Strong proficiency in SQL and programming languages such as Python and/or Scala.
Experience designing and managing semantic models, metric layers, or business-friendly data abstractions.
Knowledge of data warehousing, dimensional modeling, and analytics best practices.
Experience developing testing and validation frameworks for analytics, reporting, or AI-driven solutions.
Understanding of large language models (LLMs), generative AI applications, prompt engineering, retrieval-augmented generation (RAG), or related AI technologies.
Experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
Strong analytical, problem-solving, and stakeholder management skills.
Ability to work effectively across cross-functional teams in a fast-paced environment.

Responsibilities

Design and curate the semantic layer that Databricks Genie relies on: table/column descriptions, synonyms, certified/trusted assets, sample questions, and metric definitions.
Build and maintain Genie Spaces, iterating on data room composition and instructions to improve response accuracy and relevance.
Define and maintain metric views and business logic so natural language queries map correctly to governed KPIs.
Develop evaluation frameworks to test Genie's query accuracy, SQL generation quality, and response correctness against ground-truth business questions.
Build regression test suites so changes to data models or Genie configuration don't silently degrade answer quality.
Analyze failure modes (ambiguous questions, join errors, misinterpreted metrics) and iterate on fixes — via schema improvements, better metadata, or prompt/instruction tuning
Work with data engineers to ensure upstream data quality and modeling decisions support reliable AI/BI outputs.
Exposure to LLM-based or generative AI systems, prompt evaluation, or RAG architectures.
Partner with the DevOps/Platform team on deployment, versioning, and monitoring of Genie Spaces and any custom AI services (CI/CD, Unity Catalog governance, observability).
Translate business stakeholder feedback on AI/BI outputs into concrete improvements to data models, prompts, or configurations.
Strong scripting/programming skills in Python and/or Scala; comfortable with SQL.
Working knowledge of at least one major cloud provider (AWS, Azure, or GCP).
Experience with semantic modeling or metric layers (dbt, LookML, or Databricks Metric Views).
Background in BI tools (Power BI, Tableau, Looker) and how they integrate with Databricks SQL.