Data Engineer (AI Pipelines)

DeWinter Group; Campbell (CA); Remote; USD 68,880 - 241,080; Part time

Job summary

A leader in AI testing is looking for a skilled Data Engineer (AI Pipelines) for a 12-month remote contract. You will build scalable ETL/ELT pipelines, ensuring clean and transformed datasets for AI applications. Candidates should have over 4 years of Data Engineering experience, with expertise in SQL, Spark, Python, and tools like Airflow and Snowflake. This role demands strong autonomy and communication to meet project goals quickly.

Qualifications

• 4+ years of experience in Data Engineering.
• Deep expertise in SQL, Spark, Python, and modern data tools.
• Ability to work autonomously and manage time effectively.

Responsibilities

• Build scalable ETL/ELT pipelines for AI training and inference.
• Implement data quality checks and automated validation.
• Manage the storage and versioning of large datasets.

Skills

SQL

Spark

Python

Airflow

dbt

Git

Data Engineering

Cloud Data Warehousing

Communication

Tools

Snowflake

BigQuery

DVC

Job description

Title: Data Engineer (AI Pipelines)
Job Type: Contract
Contract Length:12 Months
Pay Range:$50/hr – $175/hr
Start Date:ASAP
Location:Remote

About the Opportunity

Our client, a leader in AI testing, is looking for a skilled Data Engineer (AI Pipelines) to join their team for a 12-month engagement. This project involves building scalable ETL/ELT pipelines to ingest, clean, and transform massive datasets for AI training, inference, and low-latency real-time applications. This is a high-impact role that requires a self-motivated professional who can hit the ground running and deliver results quickly.

Key Responsibilities & Deliverables

• Building scalable ETL/ELT pipelines to ingest, clean, and transform massive datasets for AI training and inference.
• Implementing data quality checks and automated validation to prevent "garbage in, garbage out" in AI systems.
• Managing the storage and versioning of large datasets using tools like DVC or Snowflake.
• Optimizing data retrieval patterns for low-latency RAG systems and real-time model serving.
• Collaborating with ML engineers to ensure data features are consistent across training and production.

Required Skills & Experience

• 4+ years of experience in Data Engineering.
• Deep expertise in SQL, Spark, Python, and modern data stack tools (Airflow, dbt). This isn't a learning role—you need to be a subject matter expert.
• Demonstrated ability to work autonomously and manage your own time effectively to meet project goals.
• Experience with cloud data warehouses (Snowflake, BigQuery) and Git.
• Strong communication skills to provide clear and concise status updates to the project team.