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Senior Data Engineer

Software Resources, Inc. Orlando, FL

Orlando, FL

Today

Contract Senior Artificial Intelligence Azure

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Job Description

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.

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