Title: Senior Data Engineer
Location: Orlando, FL 32830/ Remote role
Duration: 24 Months Contract
Note : It is currently remote; however, this may change in future to 2-4 Days onsite. but candidates should be local and able to commute to the office without any issues if onsite requirements change.
Role Summary:
- The Senior Data Engineer – B2B AI & Data Products will lead the design, development, and implementation of B2B integrated data solutions that power analytics, reporting, and AI-driven experiences. This role will be responsible for creating trusted, scalable data foundations while enabling next-generation self-service capabilities through AI-powered applications, conversational agents, semantic search, and intelligent data products.
- Working across business, product, analytics, and technology teams, this role will architect and engineer a modern integrated data ecosystem that makes information more accessible, discoverable, and actionable. The ideal candidate combines deep data engineering expertise with hands-on experience enabling AI and generative AI solutions within data ecosystem
Key Responsibilities:
Data Platform Engineering
- Design, build, and optimize scalable data pipelines and integration frameworks within the existing DXT ecosystem in accordance with DXT data standards, to support multiple B2B data products / source systems and enterprise reporting needs.
- Architect and implement data ingestion, transformation, and storage patterns across cloud and hybrid data environments.
- Establish reusable data engineering standards and best practices to enable consistency and scalability across product domains.
- Develop curated enterprise datasets that serve as trusted sources for dashboards, analytics, and AI initiatives.
AI Data Products & Agent Enablement:
- Design and implement data architectures that support enterprise AI applications, conversational agents, and
- intelligent self-service experiences.
- Develop and optimize datasets, metadata structures, semantic layers, and knowledge repositories that enable natural language access to enterprise information.
- Build and maintain Retrieval-Augmented Generation (RAG) frameworks and semantic search capabilities supporting AI-powered data discovery.
- Engineer scalable solutions that integrate structured and unstructured data into AI-ready environments.
- Partner with business stakeholders to translate data accessibility challenges into AI-enabled solutions.
- Enable enterprise users to discover, understand, and consume trusted data assets through conversational and self-service interfaces.
- Design and implement vectorized data architectures and embedding strategies supporting LLM-based applications.
- Collaborate with AI and analytics teams to operationalize AI-driven use cases while ensuring governance, security, and compliance standards are maintained.
- Evaluate emerging AI technologies and recommend approaches that improve enterprise data accessibility, usability, and business value.
AI-Enabled Data Platform & Advanced Analytics Support:
- Design and implement scalable AI-ready data pipelines supporting 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 supporting 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.
Reporting & Analytics Data Foundations:
- Design and maintain data layers that support executive dashboards, operational KPIs, and enterprise reporting.
- Ensure data quality, lineage, and performance standards are met for datasets consumed by BI platforms, AI tools, and downstream analytical solutions.
- Collaborate with analytics teams to optimize data structures for AI enablement, visualization, self-service analytics, and advanced modeling.
Data Governance, Quality, and Reliability:
· Implement data validation, monitoring, and observability processes to ensure reliable and trusted data delivery.
· Maintain documentation, metadata standards, and data definitions supporting enterprise governance and compliance requirements.
· Proactively identify opportunities to improve pipeline performance, data usability, and architectural efficiency.
Platform Evolution & Innovation:
- Support modernization initiatives including cloud data platform expansion, automation, and AI readiness.
- Evaluate and implement modern technologies and approaches that enhance data scalability, resilience, and time-to insight.
- Contribute to the evolution of the organization’s enterprise data strategy and operating model maturity.
Minimum Qualifications:
- 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 techniques.
- Experience developing solutions that support Generative AI, LLMs, AI Assistants, Copilots, or Conversational AI applications.
- Experience designing data architectures for Retrieval-Augmented Generation (RAG) or semantic search solutions.
- Familiarity with vector databases, embeddings, semantic indexing, and knowledge retrieval architectures.
- Experience integrating structured and unstructured enterprise data sources to support 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 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.
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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