Sr. Engineer- #26-23202

Us Tech Solutions; Orlando (FL); On-site; USD 105,000 - 149,000; Full time

Job summary

US Tech Solutions is seeking a Senior Data Engineer – B2B AI & Data Products in Orlando to lead the design of a modern, integrated data ecosystem that supports analytics, AI-driven experiences, and self-service access.

The role focuses on data platform engineering, AI data products, and governance across cloud and hybrid environments, with collaboration across business, product, analytics, and technology teams. This is a 12-month contract position based in Orlando.

Qualifications

• 7+ years of experience in data engineering, data architecture, or enterprise data platform development.
• Proven experience designing and supporting enterprise data pipelines and data warehouse / lakehouse solutions.
• Strong expertise in SQL and Python.
• Experience with cloud data platforms (Snowflake, AWS) and hybrid data integration patterns.
• Hands-on experience with ETL / ELT orchestration tools and data pipeline automation.
• Strong understanding of data modeling, semantic layer design, and performance optimization techniques.
• Experience developing solutions that support Generative AI, LLMs, AI Assistants, Copilots, or Conversational AI applications.
• Experience designing data architectures for Retrieval-Augmented Generation (RAG) or semantic search solutions.

Responsibilities

• Data Platform Engineering: design, build, and optimize scalable data pipelines within the DXT ecosystem.
• Architect data ingestion, transformation, and storage across cloud and hybrid environments.
• Establish reusable data engineering standards for consistency and scalability.
• Develop curated enterprise datasets for dashboards, analytics, and AI initiatives.
• AI Data Products & Agent Enablement: design data architectures for enterprise AI applications and conversational agents.
• Develop datasets, metadata structures, semantic layers, and knowledge repositories for natural language access.
• Build and maintain RAG frameworks and semantic search capabilities.
• AI-Enabled Data Platform & Advanced Analytics Support: design pipelines for ML, AI, and predictive analytics; enable real-time architectures.
• Reporting & Analytics Foundations: maintain data layers for dashboards and KPIs; ensure data quality and lineage.
• Data Governance, Quality, and Reliability: implement validation, monitoring, and observability.
• Platform Evolution & Innovation: support cloud modernization, automation, and AI readiness.

Skills

SQL

Python

Data modeling

ETL/ELT

Cloud platforms

RAG/semantic search

LLM/AI pipelines

AI governance

Education

Bachelor’s Degree in CS/related field

Tools

Snowflake

AWS

Vector databases

ETL orchestration tools

Job description

$92 per hour

Orlando, FL

Contract

Duration: 12 months

Role Summary:

The Senior Data Engineer – B2B AI & Data Products will lead the design, development, and implementation of B2B integrated data solutions that power analytics, reporting, and AI-driven experiences. This role will be responsible for creating trusted, scalable data foundations while enabling next-generation self-service capabilities through AI-powered applications, conversational agents, semantic search, and intelligent data products. Working across business, product, analytics, and technology teams, this role will architect and engineer a modern integrated data ecosystem that makes information more accessible, discoverable, and actionable. The ideal candidate combines deep data engineering expertise with hands-on experience enabling AI and generative AI solutions within data ecosystem.

Key Responsibilities

• Data Platform Engineering




• Design, build, and optimize scalable data pipelines and integration frameworks within the existing DXT ecosystem in accordance with DXT data standards, to support multiple B2B data products / source systems and enterprise reporting needs.




• Architect and implement data ingestion, transformation, and storage patterns across cloud and hybrid data environments.




• Establish reusable data engineering standards and best practices to enable consistency and scalability across product domains.




• Develop curated enterprise datasets that serve as trusted sources for dashboards, analytics, and AI initiatives.




• AI Data Products & Agent Enablement




• Design and implement data architectures that support enterprise AI applications, conversational agents, and intelligent self-service experiences.




• Develop and optimize datasets, metadata structures, semantic layers, and knowledge repositories that enable natural language access to enterprise information.




• Build and maintain Retrieval-Augmented Generation (RAG) frameworks and semantic search capabilities supporting AI-powered data discovery.




• Engineer scalable solutions that integrate structured and unstructured data into AI-ready environments.




• Partner with business stakeholders to translate data accessibility challenges into AI-enabled solutions.




• Enable enterprise users to Client, understand, and consume trusted data assets through conversational and self-service interfaces.




• Design and implement vectorized data architectures and embedding strategies supporting LLM-based applications.




• Collaborate with AI and analytics teams to operationalize AI-driven use cases while ensuring governance, security, and compliance standards are maintained.




• Evaluate emerging AI technologies and recommend approaches that improve enterprise data accessibility, usability, and business value.




• AI-Enabled Data Platform & Advanced Analytics Support




• Design and implement scalable AI-ready data pipelines supporting machine learning, generative AI, predictive analytics, intelligent automation, and agentic AI solutions.




• Develop data products optimized for LLM consumption, semantic search, AI-assisted analytics, and natural language querying.




• Create reusable frameworks supporting AI model training, inference, orchestration, monitoring, and lifecycle management.




• Integrate cloud AI services, large language models, vector databases, and enterprise knowledge platforms into the broader data ecosystem.




• Enable real-time and event-driven data architectures that support AI-powered decision making.




• Reporting & Analytics Data Foundations




• Design and maintain data layers that support executive dashboards, operational KPIs, and enterprise reporting.




• Ensure data quality, lineage, and performance standards are met for datasets consumed by BI platforms, AI tools, and downstream analytical solutions.




• Collaborate with analytics teams to optimize data structures for AI enablement, visualization, self-service analytics, and advanced modeling.




• Data Governance, Quality, and Reliability




• Implement data validation, monitoring, and observability processes to ensure reliable and trusted data delivery.




• Maintain documentation, metadata standards, and data definitions supporting enterprise governance and compliance requirements.




• Proactively identify opportunities to improve pipeline performance, data usability, and architectural efficiency.




• Platform Evolution & Innovation




• Support modernization initiatives including cloud data platform expansion, automation, and AI readiness.




• Evaluate and implement modern technologies and approaches that enhance data scalability, resilience, and time-to insight.




• Contribute to the evolution of the organization’s enterprise data strategy and operating model maturity.

Minimum Qualifications

• 7 years of experience in data engineering, data architecture, or enterprise data platform development.




• Proven experience designing and supporting enterprise data pipelines and data warehouse / Lakehouse solutions.




• Strong expertise in SQL and Python.




• Experience with cloud data platforms (e.g., Snowflake, AWS, Clienture) and hybrid data integration patterns.




• Hands-on experience with ETL / ELT orchestration tools and data pipeline automation.




• Strong understanding of data modeling, semantic layer design, and performance optimization techniques.




• Experience developing solutions that support Generative AI, LLMs, AI Assistants, Copilots, or Conversational AI applications.




• Experience designing data architectures for Retrieval-Augmented Generation (RAG) or semantic search solutions.




• Familiarity with vector databases, embeddings, semantic indexing, and knowledge retrieval architectures.




• Experience integrating structured and unstructured enterprise data sources to support AI-driven applications.




• Strong understanding of AI governance, prompt engineering concepts, model evaluation, and responsible AI practices.




• Experience with modern AI frameworks and services such as Claude, Cursor, Snowflake Cortex AI, Databricks Mosaic AI, AmClienton Bedrock, or equivalent technologies.




• Experience implementing metadata-driven architectures that improve data discoverability and AI consumption.




• Experience supporting BI and analytics platforms such as Power BI, Tableau, or similar tools.




• Familiarity with data governance, metadata management, and data quality frameworks.




• Ability to collaborate effectively across product teams, engineering disciplines, and business stakeholders.




• Strong analytical thinking, problem-solving capability, and communication skills.

Preferred Qualifications

• Experience supporting enterprise data product models or platform-based operating structures.




• Hands-on experience enabling AI or machine learning workflows within enterprise data environments, including support for model data pipelines, intelligent data products, or automated insight generation.




• Experience supporting AI product development from concept through production deployment.




• Experience building enterprise conversational agents, AI assistants, or knowledge retrieval platforms.




• Hands-on experience implementing RAG architectures and vector search platforms.




• Experience with GraphRAG, knowledge graphs, semantic modeling, or enterprise ontologies.




• Experience enabling natural language interaction with business datasets and analytics platforms.




• Experience using agents and orchestration frameworks such as LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar technologies.




• Experience partnering with Product Managers to deliver AI-driven self-service capabilities.




• Exposure to machine learning data preparation, AI data pipelines, or advanced analytics environments.




• Experience implementing data observability or data reliability engineering practices.




• Background working in Agile delivery models with cross-functional product teams.

Education

• Bachelor’s Degree in Computer Science, Information Systems, Engineering, or related field — or equivalent professional experience.

About US Tech Solutions:

US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com .

US Tech Solutions is an Equal Opportunity Employer.

All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

AI Statement:

By applying, you acknowledge that AI-assisted tools may be used during hiring.