Senior Data & AI Engineer

Job Information

• Date Opened 09/30/2026
• Industry Technology
• Job Type Full time
• Years of Experience 10
• City Carmel
• State/Province Indiana
• Country United States
• Zip/Postal Code 46032

About Us

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

Senior Data & AI Engineer

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

About the Role

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.

What You'll Do

Schema & Data Modeling

• 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.

Semantic Layer & AI Enablement

• 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.

Business Understanding

• 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.

Quality & Collaboration

• 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.

Requirements

What You Bring

Must-Have

• 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-Have

• 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.

What Success Looks Like (First 6 Months)

• Semantic models and metadata are delivered for at least one accelerator or client use case.
• AI-generated query accuracy measurably improves on the datasets you own.
• Schema documentation is good enough that others on the team can pick it up and run with it.
• Stakeholders trust you to understand both their data and their business.