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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

Senior Data Engineer (B2B AI & Data Products Enablement) on a contract basis, onsite 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 for multiple B2B data products and source systems.
Architect and implement data ingestion, transformation, and storage patterns across cloud and hybrid environments.
Define reusable data engineering standards and best practices to support consistency and scalability across product domains.
Create curated enterprise datasets used as trusted sources for dashboards, analytics, and AI initiatives.
Design data architectures that support enterprise AI applications, conversational agents, and intelligent self-service experiences.
Develop 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-driven data discovery.
Engineer solutions integrating structured and unstructured data into AI-ready environments.
Work with business stakeholders to translate data accessibility needs into AI-enabled solutions and conversational/self-service experiences.
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.
Evaluate emerging AI technologies and recommend approaches to 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.
Develop data products optimized for LLM consumption, semantic search, AI-assisted analytics, and natural language querying.
Create 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 broader 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 to ensure trusted delivery.
Maintain documentation, metadata standards, and data definitions aligned to enterprise governance and compliance.
Proactively identify opportunities to improve pipeline performance, data usability, and architectural efficiency.
Support modernization initiatives including cloud data platform expansion, automation, and AI readiness.
Evaluate and implement modern technologies that enhance data scalability, resilience, and time-to-insight.
Contribute to the evolution of the organization’s 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 (e.g., 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.
Experience developing solutions supporting Generative AI, LLMs, AI Assistants, Copilots, or Conversational AI.
Experience designing data architectures for RAG or 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 such as Claude, Cursor, Snowflake Cortex AI, Databricks Mosaic AI, Amazon Bedrock, or equivalent.
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.
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 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.

Education

Bachelor’s Degree in Computer Science, Information Systems, Engineering, or related field, or equivalent professional experience.

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