Senior Data & AI Engineer

Carmel, United States | Posted on 09/30/2026

Founded in 2015, RADcube is a leading technology consulting and software development firm headquartered in Carmel, Indiana. The company specializes in transforming enterprise ideas into real-world innovations by leveraging emerging technologies such as Artificial Intelligence, Blockchain, and Cloud Computing.

With nearly a decade of industry experience, RADcube serves diverse sectors, including healthcare, finance, government, and manufacturing. Their core service portfolio includes:

• Digital Transformation and strategy consulting.
• Custom Software Development tailored to specific business needs.
• Advanced Data Analytics and AI-driven platforms.
• Cybersecurity and risk management.

Recognized for its innovation-led culture, RADcube operates RADlabs, an R&D hub focused on high-impact solutions like Responsible AI and Intelligent Automation. The firm is committed to a human-centric approach, ensuring cutting-edge technology delivers measurable business outcomes and long-term success for global clients.

The company’s commitment to innovation has earned significant industry honors:

2026 TechPoint Mira Awards Finalist: Named a finalist for Tech Company of the Year, recognizing high-growth pioneers that demonstrate extraordinary leadership.

Public Sector Excellence: Awarded the Utah NASPO Cloud & Software Solutions Contract, solidifying their role as a trusted partner for large-scale government digital initiatives and more.

Job Description

Location: Carmel, Indiana
Experience: 6–10 years
Employment Type: Full-time

About the Role

RADcube is hiring a hands-onSenior Engineer who knows data, AI, and the business. You will dig into complexenterprise schemas, work out what the data means to the business, and build the models, semantic layers, and metadata that let AI systems answer questionsaccurately. You will contribute directly to our RADLabs accelerators, includinggenerative BI and agentic platforms, and to client work in pharma, lifesciences, and healthcare.

What You'll Do

Schema & Data Modeling

• Build and maintain data models(dimensional, relational, lakehouse) that follow team standards.
• Explore and document unfamiliar orlegacy schemas, producing ER diagrams, data dictionaries, join paths, andlineage.
• Develop and optimize SQL,transformations, and pipelines on cloud data platforms.

Semantic Layer & AIEnablement

• Translate raw tables into business-friendlysemantic models: metrics, dimensions, hierarchies, and relationships.
• Write and enrich schema metadata anddescriptions 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 queriesfor correctness, and help build test sets and guardrails.

Business Understanding

• Take part in client discovery sessionsto understand processes, KPIs, and reporting needs.
• Turn business questions into datarequirements and validate metric definitions with stakeholders.
• Explain data findings clearly to bothtechnical and non-technical audiences.

Quality & Collaboration

• Apply data quality checks, namingstandards, and documentation practices.
• Follow governance and compliancerequirements (GxP, HIPAA) where relevant.
• Review peers' work and support juniorengineers when needed.

Requirements

What You Bring

Must-Have

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

Nice-to-Have

• Experience in pharma, life sciences,manufacturing and quality, or healthcare data.
• dbt, or semantic layer tools such asCube, dbt Semantic Layer, or LookML.
• Familiarity with vector databases,knowledge graphs, or agentic frameworks (LangChain/LangGraph, BedrockAgents, MCP).
• Data catalog tools such as UnityCatalog, Collibra, or AWS DataZone.
• AWS, Azure, or Databrickscertifications.

What Success Looks Like (First 6 Months)

• Semantic models and metadata aredelivered for at least one accelerator or client use case.
• AI-generated query accuracy measurablyimproves on the datasets you own.
• Schema documentation is good enough thatothers on the team can pick it up and run with it.
• Stakeholders trust you to understandboth their data and their business.