Senior Data & AI Engineer

RADcube - A NLogix Company•Carmel, IN

8d•Onsite

About The Position

RADcube is hiring a hands-on Senior Engineer who knows data, AI, and the business. You will dig into complex enterprise schemas, work out what the data means to the business, and build the models, semantic layers, and metadata that let AI systems answer questions accurately. You will contribute directly to our RADLabs accelerators, including generative BI and agentic platforms, and to client work in pharma, life sciences, and healthcare.

Requirements

6+ years in data engineering, analytics engineering, or BI development.
Strong SQL and solid understanding of relational and dimensional modeling.
Demonstrated ability to learn and navigate large enterprise schemas (SAP, Salesforce, MES, or similar).
Hands-on experience with AWS (Redshift, Glue, Athena, S3) and/or Azure (Synapse, Fabric, Data Factory), plus Databricks or Snowflake.
Proficiency in Python for data work.
Practical exposure to LLMs on structured data, such as text-to-SQL, semantic layers, or AI-assisted analytics.
Good business sense and comfort talking with stakeholders about KPIs and processes.

Nice To Haves

Experience in pharma, life sciences, manufacturing and quality, or healthcare data.
dbt, or semantic layer tools such as Cube, dbt Semantic Layer, or LookML.
Familiarity with vector databases, knowledge graphs, or agentic frameworks (LangChain/LangGraph, Bedrock Agents, MCP).
Data catalog tools such as Unity Catalog, Collibra, or AWS DataZone.
AWS, Azure, or Databricks certifications.

Responsibilities

Build and maintain data models (dimensional, relational, lakehouse) that follow team standards.
Explore and document unfamiliar or legacy schemas, producing ER diagrams, data dictionaries, join paths, and lineage.
Develop and optimize SQL, transformations, and pipelines on cloud data platforms.
Translate raw tables into business-friendly semantic models: metrics, dimensions, hierarchies, and relationships.
Write and enrich schema metadata and descriptions to improve LLM text-to-SQL and generative BI accuracy.
Work with AI engineers on RAG pipelines, agent tools, and prompt design where structured data is involved.
Test and evaluate AI-generated queries for correctness, and help build test sets and guardrails.
Take part in client discovery sessions to understand processes, KPIs, and reporting needs.
Turn business questions into data requirements and validate metric definitions with stakeholders.
Explain data findings clearly to both technical and non-technical audiences.
Apply data quality checks, naming standards, and documentation practices.
Follow governance and compliance requirements (GxP, HIPAA) where relevant.
Review peers' work and support junior engineers when needed.