Senior Data Engineer

Software Resources, Inc. is hiring a Senior Data Engineer (B2B AI & Data Products Enablement) for an onsite contract role in Orlando, FL.

Responsibilities

• Design, build, and optimize scalable data pipelines and integration frameworks within the existing DXT ecosystem in line with DXT data standards
• Support multiple B2B data products and source systems, plus enterprise reporting requirements
• Architect and implement ingestion, transformation, and storage patterns across cloud and hybrid data environments
• Define reusable data engineering standards and best practices for consistent, scalable delivery across product domains
• Create curated enterprise datasets that act as trusted sources for dashboards, analytics, and AI initiatives
• Design data architectures that enable enterprise AI applications, conversational agents, and intelligent self-service experiences
• Develop and optimize datasets, metadata structures, semantic layers, and knowledge repositories for natural language access to enterprise information
• Build and maintain Retrieval-Augmented Generation (RAG) frameworks and semantic search capabilities for AI-powered data discovery
• Engineer solutions integrating structured and unstructured data into AI-ready environments
• Partner with business stakeholders to convert 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 for LLM-based applications
• Collaborate with AI and analytics teams to operationalize AI-driven use cases while maintaining governance, security, and compliance standards
• Evaluate emerging AI technologies and recommend approaches that improve enterprise data accessibility, usability, and business value
• Design and implement scalable AI-ready data pipelines for machine learning, generative AI, predictive analytics, intelligent automation, and agentic AI solutions
• Create data products optimized for LLM consumption, semantic search, AI-assisted analytics, and natural language querying
• Develop reusable frameworks for AI model training, inference, orchestration, monitoring, and lifecycle management
• Integrate cloud AI services, large language models, vector databases, and enterprise knowledge platforms into the data ecosystem
• Enable real-time and event-driven data architectures that support AI-powered decision making
• Design and maintain data layers for executive dashboards, operational KPIs, and enterprise reporting
• Ensure data quality, lineage, and performance standards for datasets used by BI platforms, AI tools, and downstream analytics
• 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 to support enterprise governance and compliance requirements
• Identify opportunities to improve pipeline performance, data usability, and architectural efficiency
• Support modernization initiatives such as cloud data platform expansion, automation, and AI readiness
• Evaluate and implement modern technologies that improve data scalability, resilience, and time-to-insight
• Contribute to evolving enterprise data strategy and operating model maturity

Requirements

• 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, 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 supporting generative AI, LLMs, AI assistants, copilots, or conversational AI applications
• Experience designing data architectures for RAG and semantic search solutions
• Familiarity with vector databases, embeddings, semantic indexing, and knowledge retrieval architectures
• Experience integrating structured and unstructured enterprise data sources for AI-driven applications
• Strong understanding of AI governance, prompt engineering concepts, model evaluation, and responsible AI practices
• Experience with modern AI frameworks and services including 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

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 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: Lead design, development, and implementation of B2B integrated data solutions that power analytics, reporting, and AI-driven experiences, including trusted data foundations and next-generation AI-enabled self-service capabilities through conversational interfaces, semantic search, and intelligent data products.