Offer summary

Qualifications:

5+ years of experience in Data Engineering or Data Infrastructure roles., Strong command of ETL frameworks like Airflow, DBT, and Spark., Proficiency in databases such as PostgreSQL and Snowflake, and cloud services like AWS., Deep knowledge of SQL performance tuning and data quality practices..

Key responsibilities:

Build scalable ETL/ELT pipelines for AI agent training and inference.

Integrate data from diverse sources including APIs and real-time streams.

Design high-performance data models and optimize SQL queries for large datasets.

Collaborate with AI/ML engineers to align data with business needs and document processes.

Job description

📊 Senior Data Engineer – Scalable Pipelines for AI Agent Infrastructure

We’re looking for a Senior Data Engineer to design, build, and maintain the data foundation behind our next-generation AI agent platform. You’ll work closely with AI/ML teams to power training, inference, and continuous learning through highly scalable data pipelines and cloud-native architectures.

If you’re passionate about data infrastructure, performance optimization, and driving data quality at scale, we want to hear from you.

🗓 Start date: ASAP
📆 Contract type: Contractor - Indefinite
🌐 Work hours: Monday to Friday, 7.30 am to 4.30 pm PST - 100% Remote

🛠️ What You’ll Be Doing

Build scalable ETL/ELT pipelines to support AI agent training and inference.
Integrate data from diverse sources (APIs, databases, real-time streams).
Design high-performance data models using PostgreSQL and Snowflake.
Optimize SQL queries for large datasets and data warehouse workloads.
Implement data validation, cleansing, and monitoring workflows.
Automate data engineering tasks and contribute to CI/CD pipelines.
Collaborate with AI/ML engineers and product teams to align data with business needs.
Document processes, pipelines, and infrastructure decisions clearly.

✅ What You Need to Succeed

Must-haves

5+ years of experience in Data Engineering or Data Infrastructure roles.
Strong command of:
- ETL frameworks: Airflow, DBT, Spark
- Databases: PostgreSQL, Snowflake
- Cloud & DevOps: AWS (S3, EC2), Linux, Docker
- CI/CD: GitHub, Jenkins or similar
- Programming: Python (data-focused scripting)
Deep knowledge of SQL performance tuning, data warehousing, and data quality practices.
Experience with data security and managing large-scale database environments.
Comfortable working in cross-functional teams and agile environments.

Nice-to-haves

Experience with AWS RDS, MWAA, Lambda.
Familiarity with vector databases and retrieval-augmented generation (RAG) pipelines.
Exposure to prompt engineering, LLM workflows, or AI/ML data operations.
Data Engineering certification (GCP, AWS, or equivalent).