Senior Data Engineer (B2B AI & Data Products Enablement)

JobDiva, Inc.; Seattle (WA); On-site; USD 170,000 - 200,000; Full time

Benefits offered by this job

Contract role

On-site Seattle/CA work locations

Job summary

Software Resources is seeking a Senior Data Engineer to lead the design, development, and deployment of B2B AI and data products in Seattle, WA or California locations. You will architect scalable data foundations, enable AI/LLM driven solutions, and collaborate with cross-functional teams to deliver trusted data assets for analytics and AI experiences.

The role emphasizes governance, security, and compliance while pushing modernization of the data platform to support real-time and AI-enabled

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.

Responsibilities

• Design, build, and optimize scalable data pipelines and integration frameworks within the existing DXT ecosystem to support multiple B2B data products 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.
• 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 access, 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.
• 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.
• Design and maintain data layers for executive dashboards, KPIs, and enterprise reporting.
• Ensure data quality, lineage, and performance standards for datasets used by BI, AI tools, and analytics solutions.
• Collaborate with analytics teams to optimize data structures for AI enablement and self-service analytics.
• Implement data validation, monitoring, and observability processes to ensure reliable data delivery.
• Maintain metadata standards and data definitions supporting governance and compliance.

Skills

SQL

Python

Snowflake Cortex AI

ETL/ELT

Data pipelines

LLMs / Generative AI

Data governance

Vectors / embeddings

Semantic search

Cloud platforms

Education

Bachelor's Degree in CS/IS/Engineering

Tools

Power BI

Tableau

LangGraph

Semantic Kernel

AutoGen

Job description

Software Resources has an immediate contract job opportunity for a Senior Data Engineer (B2B AI & Data Products Enablement) with a major corporation in Seattle, WA or Glendale/Burbank/Anaheim, CA.

12+ month contract

Pay Rate: $85 - $90/hr DOE

Must Haves

• AI development experience and experience with Snowflake Cortex/Cortex AI

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 Client, 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.