R
Senior Data & AI Engineer
RADcube
2 hours ago
Full-time
On-site
Carmel, Indiana, United States
RADcube is hiring a
Senior Data & AI Engineer
in
Carmel, Indiana
(onsite). The role focuses on data modeling, semantic layers, and metadata foundations so AI systems can answer questions accurately, including text-to-SQL and RAG-style workflows, alongside client discovery and stakeholder alignment.
Responsibilities
Build and maintain
data models
including dimensional, relational, and lakehouse designs that align with team standards.
Investigate and document
unfamiliar or legacy schemas
by producing ER diagrams, data dictionaries, join paths, and lineage.
Develop and optimize
SQL, transformations, and data pipelines
on cloud data platforms.
Convert raw tables into
business-facing semantic models
covering metrics, dimensions, hierarchies, and relationships.
Write and enrich
schema metadata and descriptions
to support improved performance for
LLM text-to-SQL
and generative BI accuracy.
Partner with AI engineers on
RAG pipelines , agent tools, and prompt design when the system relies on structured data.
Test and evaluate
AI-generated queries
for correctness, including contributions to test sets and guardrails.
Participate in
client discovery
to understand business processes, KPIs, and reporting requirements.
Translate business questions into data requirements and validate
metric definitions
with stakeholders.
Communicate data findings clearly to both technical and non-technical audiences.
Apply
data quality checks , naming standards, and documentation practices.
Follow relevant
governance and compliance
requirements (GxP, HIPAA) where applicable.
Review peers’ work and support junior engineers when needed.
Requirements
6+ years
of experience in data engineering, analytics engineering, or BI development.
Strong
SQL
skills and a solid understanding of
relational and dimensional modeling .
Proven ability to learn and navigate large enterprise schemas (for example,
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 , including text-to-SQL, semantic layers, or AI‑assisted analytics.
Good business sense and comfort discussing KPIs and processes with stakeholders.
Technologies SQL, Python, AWS (Redshift, Glue, Athena, S3), Azure (Synapse, Fabric, Data Factory), Databricks, Snowflake, SAP, Salesforce, MES, LLMs, text‑to‑SQL, RAG, LangChain, LangGraph, Bedrock Agents, MCP, Unity Catalog, Collibra, AWS DataZone, dbt, Cube, dbt Semantic Layer, LookML, vector databases, knowledge graphs, agentic frameworks.
What You Bring (Nice‑to‑Have)
Experience with pharma, life sciences, manufacturing and quality, or healthcare data.
Experience with
dbt
or semantic layer tooling such as
Cube ,
dbt Semantic Layer , or
LookML .
Familiarity with vector databases, knowledge graphs, or agentic frameworks (LangChain/LangGraph, Bedrock Agents, MCP).
Experience with 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 delivered for at least one
accelerator
or
client use case .
Measurable improvement in
AI-generated query accuracy
on datasets you own.
Schema documentation that enables other team members to adopt and use it.
Stakeholder confidence that you can understand both their data and their business context.
#J-18808-Ljbffr
Senior Data & AI Engineer
in
Carmel, Indiana
(onsite). The role focuses on data modeling, semantic layers, and metadata foundations so AI systems can answer questions accurately, including text-to-SQL and RAG-style workflows, alongside client discovery and stakeholder alignment.
Responsibilities
Build and maintain
data models
including dimensional, relational, and lakehouse designs that align with team standards.
Investigate and document
unfamiliar or legacy schemas
by producing ER diagrams, data dictionaries, join paths, and lineage.
Develop and optimize
SQL, transformations, and data pipelines
on cloud data platforms.
Convert raw tables into
business-facing semantic models
covering metrics, dimensions, hierarchies, and relationships.
Write and enrich
schema metadata and descriptions
to support improved performance for
LLM text-to-SQL
and generative BI accuracy.
Partner with AI engineers on
RAG pipelines , agent tools, and prompt design when the system relies on structured data.
Test and evaluate
AI-generated queries
for correctness, including contributions to test sets and guardrails.
Participate in
client discovery
to understand business processes, KPIs, and reporting requirements.
Translate business questions into data requirements and validate
metric definitions
with stakeholders.
Communicate data findings clearly to both technical and non-technical audiences.
Apply
data quality checks , naming standards, and documentation practices.
Follow relevant
governance and compliance
requirements (GxP, HIPAA) where applicable.
Review peers’ work and support junior engineers when needed.
Requirements
6+ years
of experience in data engineering, analytics engineering, or BI development.
Strong
SQL
skills and a solid understanding of
relational and dimensional modeling .
Proven ability to learn and navigate large enterprise schemas (for example,
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 , including text-to-SQL, semantic layers, or AI‑assisted analytics.
Good business sense and comfort discussing KPIs and processes with stakeholders.
Technologies SQL, Python, AWS (Redshift, Glue, Athena, S3), Azure (Synapse, Fabric, Data Factory), Databricks, Snowflake, SAP, Salesforce, MES, LLMs, text‑to‑SQL, RAG, LangChain, LangGraph, Bedrock Agents, MCP, Unity Catalog, Collibra, AWS DataZone, dbt, Cube, dbt Semantic Layer, LookML, vector databases, knowledge graphs, agentic frameworks.
What You Bring (Nice‑to‑Have)
Experience with pharma, life sciences, manufacturing and quality, or healthcare data.
Experience with
dbt
or semantic layer tooling such as
Cube ,
dbt Semantic Layer , or
LookML .
Familiarity with vector databases, knowledge graphs, or agentic frameworks (LangChain/LangGraph, Bedrock Agents, MCP).
Experience with 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 delivered for at least one
accelerator
or
client use case .
Measurable improvement in
AI-generated query accuracy
on datasets you own.
Schema documentation that enables other team members to adopt and use it.
Stakeholder confidence that you can understand both their data and their business context.
#J-18808-Ljbffr