Senior Data Engineer – B2B AI & Data Products | Orlando, FL - Onsite | 12 Months | Video

Stellent IT LLC; Orlando (FL); On-site; USD 120,000 - 180,000; Full time

Job summary

Stellent IT LLC in Orlando, FL is seeking a Senior Data Engineer B2B AI & Data Products to design, build, and implement scalable data solutions for analytics, reporting, and AI-powered applications. This role blends senior data engineering and data architecture with hands-on work supporting Generative AI, LLMs, RAG, semantic search, vector databases, and conversational AI.

You will collaborate with business, product, analytics, AI, and technology teams to enable self-service experiences using

Qualifications

• 7+ years of experience in data engineering, data architecture, or enterprise data platform development.
• Strong hands-on expertise in SQL and Python.
• Experience designing enterprise data pipelines, data warehouses, and/or Lakehouse solutions.
• Experience with cloud data platforms such as Snowflake, AWS, Azure, or equivalent.
• Experience with ETL/ELT orchestration and pipeline automation.
• Strong understanding of data modeling, semantic layer design, and performance optimization.
• Hands-on experience supporting Generative AI, LLMs, AI Assistants, Copilots, or Conversational AI.
• Experience designing RAG and/or semantic search architectures.
• Familiarity with vector databases, embeddings, semantic indexing, and knowledge retrieval.
• Experience integrating structured and unstructured data for AI applications.
• Understanding of AI governance, prompt engineering, model evaluation, and responsible AI concepts.
• Experience with modern AI frameworks/services such as Claude, Cursor, Snowflake Cortex AI, Databricks Mosaic AI, Amazon Bedrock, or equivalent technologies.
• Experience with metadata-driven architectures and data discoverability.
• Experience supporting BI platforms such as Power BI, Tableau, or similar.
• Knowledge of data governance, metadata management, and data quality frameworks.
• Strong communication, analytical, problem-solving, and cross-functional collaboration skills.

Responsibilities

• Design, develop, and optimize scalable data pipelines and integration frameworks.
• Build data ingestion, transformation, and storage solutions across cloud and hybrid environments.
• Develop reusable data engineering standards, frameworks, and best practices.
• Create curated enterprise datasets for reporting, analytics, dashboards, and AI applications.
• Work with multiple B2B data products and source systems within the enterprise data ecosystem.
• Design data architectures supporting Generative AI, LLM applications, conversational agents, AI assistants, and intelligent self-service solutions.
• Build and optimize datasets, metadata, semantic layers, and knowledge repositories for natural-language access to enterprise data.
• Develop and maintain RAG and semantic search capabilities.
• Integrate structured and unstructured enterprise data into AI-ready environments.
• Design vectorized data architectures, embedding strategies, semantic indexing, and knowledge retrieval solutions.
• Support AI-driven data discovery and natural-language querying.
• Develop scalable pipelines supporting ML, Generative AI, predictive analytics, intelligent automation, and agentic AI.
• Build data products optimized for LLM consumption and AI-assisted analytics.
• Integrate cloud AI services, LLMs, vector databases, and enterprise knowledge platforms.
• Support AI model training, inference, orchestration, monitoring, and lifecycle requirements.
• Enable real-time and event-driven data architectures where required.
• Build and maintain data layers supporting executive dashboards, operational KPIs, and enterprise reporting.
• Ensure data quality, lineage, performance, and reliability for BI and AI consumers.
• Partner with analytics teams to optimize data structures for visualization, self-service analytics, and advanced modeling.
• Implement data validation, monitoring, observability, and reliability processes.
• Maintain metadata, documentation, data definitions, and governance standards.
• Ensure AI and data solutions comply with applicable security, governance, and responsible AI requirements.
• Identify opportunities to improve pipeline performance, data usability, scalability, and architecture.
• Contribute to cloud data platform modernization and AI-readiness initiatives.
• Evaluate emerging data and AI technologies and recommend solutions that improve scalability and business value.
• Help evolve enterprise data strategy, architecture, and operating-model maturity.

Skills

SQL

Python

Data modeling

Data pipelines

Cross-functional collaboration

Education

Bachelor's degree in Computer Science, Information Systems, Engineering, or related field

Tools

Snowflake

AWS

Azure

ETL/ELT tooling

Vector databases

Job description

Title: Senior Data Engineer B2B AI & Data Products

Location: Orlando, FL - Onsite

Duration: 12 Months

MOI: 2 interview rounds

A 3rd round may be added if needed

Position Overview

We are seeking a Senior Data Engineer B2B AI & Data Products to design, build, and implement scalable enterprise data solutions that support analytics, reporting, and AI-powered applications.

This role combines senior-level data engineering and data architecture with hands-on experience supporting Generative AI, LLMs, RAG, semantic search, vector databases, and conversational AI.

The selected candidate will work closely with business, product, analytics, AI, and technology teams to build trusted data foundations and enable modern self-service experiences where users can discover, understand, and consume enterprise information through AI-powered applications.

Key Responsibilities

Data Platform & Engineering

• Design, develop, and optimize scalable data pipelines and integration frameworks.
• Build data ingestion, transformation, and storage solutions across cloud and hybrid environments.
• Develop reusable data engineering standards, frameworks, and best practices.
• Create curated enterprise datasets for reporting, analytics, dashboards, and AI applications.
• Work with multiple B2B data products and source systems within the enterprise data ecosystem.

AI Data Products & RAG

• Design data architectures supporting Generative AI, LLM applications, conversational agents, AI assistants, and intelligent self-service solutions.
• Build and optimize datasets, metadata, semantic layers, and knowledge repositories for natural-language access to enterprise data.
• Develop and maintain RAG (Retrieval-Augmented Generation) and semantic search capabilities.
• Integrate structured and unstructured enterprise data into AI-ready environments.
• Design vectorized data architectures, embedding strategies, semantic indexing, and knowledge retrieval solutions.
• Support AI-driven data discovery and natural-language querying.

AI-Ready Data Platforms

• Develop scalable pipelines supporting machine learning, Generative AI, predictive analytics, intelligent automation, and agentic AI.
• Build data products optimized for LLM consumption and AI-assisted analytics.
• Integrate cloud AI services, LLMs, vector databases, and enterprise knowledge platforms.
• Support AI model training, inference, orchestration, monitoring, and lifecycle requirements.
• Enable real-time and event-driven data architectures where required.

Reporting & Analytics

• Build and maintain data layers supporting executive dashboards, operational KPIs, and enterprise reporting.
• Ensure data quality, lineage, performance, and reliability for BI and AI consumers.
• Partner with analytics teams to optimize data structures for visualization, self-service analytics, and advanced modeling.

Governance & Reliability

• Implement data validation, monitoring, observability, and reliability processes.
• Maintain metadata, documentation, data definitions, and governance standards.
• Ensure AI and data solutions comply with applicable security, governance, and responsible AI requirements.
• Identify opportunities to improve pipeline performance, data usability, scalability, and architecture.

Innovation & Modernization

• Contribute to cloud data platform modernization and AI-readiness initiatives.
• Evaluate emerging data and AI technologies and recommend solutions that improve scalability and business value.
• Help evolve enterprise data strategy, architecture, and operating-model maturity.

Required Qualifications

• 7+ years of experience in data engineering, data architecture, or enterprise data platform development.
• Strong hands-on expertise in SQL and Python.
• Experience designing enterprise data pipelines, data warehouses, and/or Lakehouse solutions.
• Experience with cloud data platforms such as Snowflake, AWS, Azure, or equivalent.
• Experience with ETL/ELT orchestration and pipeline automation.
• Strong understanding of data modeling, semantic layer design, and performance optimization.
• Hands-on experience supporting Generative AI, LLMs, AI Assistants, Copilots, or Conversational AI.
• Experience designing RAG and/or semantic search architectures.
• Familiarity with vector databases, embeddings, semantic indexing, and knowledge retrieval.
• Experience integrating structured and unstructured data for AI applications.
• Understanding of AI governance, prompt engineering, model evaluation, and responsible AI concepts.
• Experience with modern AI frameworks/services such as Claude, Cursor, Snowflake Cortex AI, Databricks Mosaic AI, Amazon Bedrock, or equivalent technologies.
• Experience with metadata-driven architectures and data discoverability.
• Experience supporting BI platforms such as Power BI, Tableau, or similar.
• Knowledge of data governance, metadata management, and data quality frameworks.
• Strong communication, analytical, problem-solving, and cross-functional collaboration skills.

Preferred Qualifications

• Experience with enterprise data product models or platform-based operating structures.
• Experience enabling AI/ML workflows within enterprise data environments.
• Experience taking AI products from concept through production.
• Experience building enterprise conversational agents, AI assistants, or knowledge retrieval platforms.
• Hands-on experience with RAG and vector search platforms.
• Experience with GraphRAG, knowledge graphs, semantic modeling, or enterprise ontologies.
• Experience enabling natural-language interaction with business data and analytics platforms.
• Experience with agent/orchestration frameworks such as LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar.
• Experience partnering with Product Managers on AI-driven self-service products.
• Exposure to ML data preparation, AI data pipelines, or advanced analytics.
• Experience with data observability/data reliability engineering.
• Agile experience working with cross-functional product teams.

Education

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience.

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