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TMS

Senior Data Architect – Treasure Data, CDP, Snowflake, Databricks, Kafka

TMS

. Design and implement production-grade enterprise data architectures .

Posted 9/22/2026contractRemote • Oregon • United StatesSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in designing and implementing production-grade enterprise data architectures, with a strong focus on building AI-ready data pipelines and supporting modern AI/ML applications. Proficient in developing ETL/ELT pipelines using Python and SQL, and integrating various cloud data platforms.

Highest-signal resume keywords
Data Engineering ExperienceETL/ELT Pipeline DevelopmentAI/Generative AI Data EngineeringReal-Time Analytics ArchitectureSnowflake and Databricks Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
PythonSQLETLELTData Architecture DesignIncremental Data ProcessingEvent-Driven ArchitectureStreaming Data PipelinesCDCDebezium
Tools & Technologies
Treasure DataAWSAzureGCPApache KafkaDatabricksSnowflake
Industry Keywords
Enterprise Data WarehousingAI/ML ApplicationsReal-Time Data ArchitecturesBatch Data ProcessingSemantic SearchVector DatabasesRAG Infrastructure

Tech Stack

Tools & technologies
ApacheAWSAzureCloudETLGoogle Cloud PlatformKafkaPythonSQL

About the role

Key responsibilities & impact
  • Design and implement production-grade enterprise data architectures
  • Build and support batch and real-time data architectures
  • Develop hands-on ETL/ELT pipelines using Python and SQL
  • Design real-time analytics, event-driven architectures, and streaming data pipelines
  • Integrate Treasure Data/CDP with enterprise data platforms
  • Build AI-ready data pipelines, semantic search, vector-search, and RAG infrastructure
  • Support modern AI/ML and Generative AI applications with enterprise data platforms
  • Work with Snowflake, Databricks, AWS, Azure, or GCP data engineering services
  • Implement CDC/Debezium and incremental data processing solutions

Requirements

What you’ll need
  • 10+ years of Data Engineering / Data Architecture experience
  • Strong hands-on ETL/ELT, Python, SQL, and Enterprise Data Warehousing experience
  • AI/Generative AI data engineering experience, including RAG, vector databases, LangChain/LangGraph, AI Agents, Azure OpenAI, AWS Bedrock, and related technologies
  • Understanding of enterprise data platforms supporting modern AI/ML and Generative AI applications
  • Treasure Data / Treasure Data CDP production experience
  • Experience with real-time analytics, event-driven architecture, and streaming data pipelines
  • Hands-on Apache Kafka experience preferred
  • Experience with Snowflake, Databricks, or equivalent cloud data platforms
  • Strong experience with AWS, Azure, or GCP data engineering services
  • CDC or Debezium and incremental data processing experience
  • Experience designing batch and real-time enterprise data architectures
  • Experience building AI-ready data pipelines, semantic search, or RAG infrastructure is a plus
  • Candidates should have production-grade data architecture design and implementation experience, not only ETL maintenance

Benefits

Comp & perks
  • Remote work
  • Contract opportunity