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. #J-18808-Ljbffr DataJobs