Senior Data Engineer

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.