Software Resources, Inc. seeks a Senior Data Engineer to lead the design, development, and implementation of B2B integrated data solutions that support analytics, reporting, and AI-driven experiences. This contract role is based onsite in Orlando, FL.
Responsibilities
- Design, build, and optimize scalable data pipelines and integration frameworks within the existing DXT ecosystem, aligned to DXT data standards, to support multiple B2B data products and source systems as well as enterprise reporting needs.
- Architect and implement ingestion, transformation, and storage patterns across cloud and hybrid data environments.
- Establish reusable data engineering standards and best practices to support consistent, scalable delivery across product domains.
- Develop curated enterprise datasets that serve as trusted sources for dashboards, analytics, and AI initiatives.
- 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 to enable natural language access to enterprise information.
- Build and maintain Retrieval-Augmented Generation (RAG) frameworks and semantic search capabilities for AI-powered data discovery.
- Engineer scalable solutions that integrate structured and unstructured data into AI-ready environments.
- Partner with business stakeholders to convert data accessibility challenges into AI-enabled solutions.
- Support enterprise users in Client-facing contexts 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 maintaining governance, security, and compliance requirements.
- Evaluate emerging AI technologies and recommend approaches that improve enterprise data accessibility, usability, and business value.
- 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.
- 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.
- Implement data validation, monitoring, and observability processes to support reliable and trusted data delivery.
- Maintain documentation, metadata standards, and data definitions aligned to enterprise governance and compliance needs.
- Identify opportunities to improve pipeline performance, data usability, and architectural efficiency.
- Support modernization efforts such as cloud data platform expansion, automation, and AI readiness.
- Evaluate and implement modern technologies and approaches to enhance data scalability, resilience, and time-to-insight.
- Contribute to the evolution of the organization’s enterprise data strategy and the maturity of its operating model.
Required 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 or Lakehouse solutions.
- Strong expertise in SQL and Python.
- Experience with cloud data platforms such as Snowflake, AWS, or Azure 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, Amazon 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.
Technologies
- SQL, Python
- Snowflake, AWS, Azure
- ETL, ELT
- Retrieval-Augmented Generation (RAG)
- Vector databases, embeddings, semantic indexing
- Generative AI, LLMs
- AI Assistants, Copilots, Conversational AI
- Claude, Cursor, Snowflake Cortex AI, Databricks Mosaic AI
- Amazon Bedrock
- Power BI, Tableau, BI platforms
- Vectorized data architectures, semantic search
- Metadata-driven architectures, conversational agents, semantic layers, knowledge repositories
- Large language models, cloud AI services, vector search
Education
Bachelor’s Degree in Computer Science, Information Systems, Engineering, or related field, or equivalent professional experience.
Job Details
- Role Type: Contract
- Location: Orlando, FL (onsite)
- Minimum Experience: 7 years
Benefits
- Medical, dental, and vision coverage
- 401(k) with company match
- Short-term disability
- Life insurance with AD&D
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.
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. The role focuses on creating trusted, scalable data foundations and 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, the engineer will architect and build a modern integrated data ecosystem that improves accessibility, discoverability, and actionability of information. The ideal candidate brings deep data engineering expertise and hands-on experience enabling AI and generative AI solutions within a data ecosystem.
