Data Engineer, Analytics

Senior Data Engineer (B2B AI & Data Products Enablement)Software Resources has an immediate contract job opportunity for a Senior Data Engineer (B2B AI & Data Products Enablement) with a major corporation in Orlando, FL. 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 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 enter