Senior Data Engineer: AI-Driven Data Platform & RAG Expert

JobDiva, Inc.

Seattle (WA)

On-site

USD 170,000 - 200,000

Full time

7 days ago

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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.