Senior Data Architect | Remote | 15+ Years of experience

Job Description

Role: Senior Data Architect

Location: CA (Remote)

Duration: 12+ Months

Experience: 15+ Years

Job Description

We are looking for a 10+ years experienced Senior Data Architect for a contract role.

• Must have strong hands-on ETL/ELT, Python, SQL, and Enterprise Data Warehousing experience.

• AI/Generative AI data engineering experience is strongly required— RAG, vector databases, LangChain/LangGraph, AI Agents, Azure OpenAI, AWS Bedrock, etc.

• We are specifically interested in candidates who understand how enterprise data platforms support modern AI/ML and Generative AI applications.

• Treasure Data / Treasure Data CDP experience is a key requirement — please prioritize candidates with real production experience.

• Strong experience with real-time analytics, event-driven architecture, and streaming data pipelines.

• Hands-on Apache Kafka experience is highly 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 is required.

• Candidate should have experience designing batch + real-time enterprise data architectures.

• Experience building AI-ready data pipelines / semantic search / RAG infrastructure will be a major plus.

• Please do not submit traditional ETL/BI only profiles.

Specific Ask: Please submit candidates who have Treasure Data + Real-Time Analytics + Enterprise

ETL/Data Warehouse experience, ideally combined with Kafka and Generative AI.

Please provide Yes/No + years of experience for each:

1. Does the candidate have 10+ years of Data Engineering / Data Architecture experience?
2. Does the candidate have strong hands-on ETL/ELT, Python, and SQL experience?
3. Does the candidate have enterprise Data Warehouse / Lakehouse experience?
4. Does the candidate have hands-on Treasure Data / Treasure Data CDP experience?
5. How many years of Treasure Data experience does the candidate have?
6. Does the candidate have real-time analytics / streaming experience?
7. Does the candidate have hands-on Apache Kafka experience?
8. Does the candidate have CDC / Debezium experience?
9. Does the candidate have strong Snowflake or Databricks experience?
10. Does the candidate have experience designing cloud data architectures on AWS, Azure, or GCP?
11. Does the candidate have Generative AI data engineering experience?

1. Does the candidate have experience building RAG or vector-search pipelines?
2. Does the candidate have experience with LangChain, LangGraph, LlamaIndex, or AI Agents?
3. Does the candidate have experience integrating enterprise data with Azure OpenAI, AWS

Bedrock, Vertex AI, Databricks Mosaic AI, or Snowflake Cortex?

1. Has the candidate personally designed and implemented production-grade data architecture,

rather than only maintaining existing ETL jobs?

Pre-Screening Questions

Q1: Does the candidate have 10+ years of Data Engineering/Data Architecture experience, with strong hands-on ETL/ELT, Python, SQL, and Enterprise Data Warehouse/Lakehouse architecture experience?

Q2: Does the candidate have hands-on production experience with Treasure Data / Treasure Data CDP, including designing, implementing, or supporting enterprise data pipelines/platforms?

Q3: Does the candidate have hands-on experience designing and implementing real-time analytics, event-driven architectures, and streaming data pipelines, including Apache Kafka?

Q4: Does the candidate have hands-on experience with CDC/incremental data processing (or Debezium) and designing enterprise cloud data architectures using AWS, Azure, GCP, Snowflake, or Databricks?

Q5: Does the candidate have hands-on experience building AI-ready data pipelines, RAG/vector-search infrastructure, or Generative AI data architectures, including technologies such as LangChain, LangGraph, LlamaIndex, AI Agents, Azure OpenAI, AWS Bedrock, Vertex AI, or similar platforms?

Additional Information

All your information will be kept confidential according to EEO guidelines.