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Data Ai Engineering Leader

  • Data Ai Engineering Leader job type Posted on: Nov 13, 2026
  • Data Ai Engineering Leader experience level Northern Base
  • Data Ai Engineering Leader employment type Minneapolis, Minnesota
  • Data Ai Engineering Leader employment type Onsite
  • Data Ai Engineering Leader salary Full-time

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Job Title :

Data Ai Engineering Leader

Job Type :

Full-Time

Job Location :

Minneapolis Minnesota United States

Remote :

No

Jobcon Logo Job Description :

Job Description

Must Have Technical/Functional Skills
Production data engineering at scale: SQL, Python, dbt, incremental processing/CDC, schema evolution, data quality
Reusable, metadata-driven or configurable ingestion, transformation, quality and delivery frameworks
Dimensional/analytical data modeling and lakehouse architecture with Delta Lake or Apache Iceberg
Semantic and knowledge architecture: semantic models, metric layers, ontologies, knowledge graphs, vector search
Applied AI in production: retrieval, conversational analytics, agent-based capabilities with evaluation and traceability
Roles & Responsibilities
Shape data and AI architecture from business problem through production
Build reusable capabilities that other engineers adopt
Take retrieval, conversational analytics and agent-based solutions beyond prototypes
Deliver secure, maintainable solutions: access controls, tenant/client isolation, CI/CD, monitoring, lineage
Code alongside engineers, review implementation and make reuse-vs-build decisions
Generic Managerial Skills, If any
Director-level technical leadership
Turn ambiguous consulting and product needs into a practical engineering approach
Guide teams and raise the team's technical capability
Challenge thinking and introduce new approaches
Stay accountable for the domain solution and explain tradeoffs made

Jobcon Logo Position Details

Posted:

Nov 13, 2026

Reference Number:

2102-42103

Employment:

Full-time

City:

Minneapolis

Job Origin:

CIEPAL_ORGANIC_FEED

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Job Description

Must Have Technical/Functional Skills
Production data engineering at scale: SQL, Python, dbt, incremental processing/CDC, schema evolution, data quality
Reusable, metadata-driven or configurable ingestion, transformation, quality and delivery frameworks
Dimensional/analytical data modeling and lakehouse architecture with Delta Lake or Apache Iceberg
Semantic and knowledge architecture: semantic models, metric layers, ontologies, knowledge graphs, vector search
Applied AI in production: retrieval, conversational analytics, agent-based capabilities with evaluation and traceability
Roles & Responsibilities
Shape data and AI architecture from business problem through production
Build reusable capabilities that other engineers adopt
Take retrieval, conversational analytics and agent-based solutions beyond prototypes
Deliver secure, maintainable solutions: access controls, tenant/client isolation, CI/CD, monitoring, lineage
Code alongside engineers, review implementation and make reuse-vs-build decisions
Generic Managerial Skills, If any
Director-level technical leadership
Turn ambiguous consulting and product needs into a practical engineering approach
Guide teams and raise the team's technical capability
Challenge thinking and introduce new approaches
Stay accountable for the domain solution and explain tradeoffs made

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