AI Data Engineer 3 [213704]

Aquent LLC; Redmond (WA); On-site; USD 96,000 - 106,000; Full time

Benefits offered by this job

Flexible work options

Job summary

Aquent Talent in Redmond, WA is seeking an AI Data Engineer 3 for an onsite role. You will build the data foundation for modern AI experiences, including synthetic data-generation systems, scalable pipelines, validation frameworks, and cloud-backed services.

You will join a supportive environment with flexible work options and opportunities to thrive both personally and professionally, applying your backend and data-engineering expertise to enterprise-scale data workflows and AI applications.

Qualifications

• 5+ years of software or data engineering experience.
• Experience with Python, Spring Boot, and .NET.
• Experience building data generation, ETL, and distributed data-processing workflows.
• Strong knowledge of LLMs and AI quality evaluation.

Responsibilities

• Design, build, and evolve synthetic data-generation systems.
• Build and operate scalable pipelines and orchestration.
• Design and improve data models, storage, and retrieval.
• Develop validation frameworks and data quality checks.
• Use cloud engineering practices to deploy and monitor systems.
• Document progress and maintain tests.

Skills

Python

Spring Boot

.NET

Backend

ETL

LLMs

Cloud engineering

Data pipelines

APIs

Observability

Tools

Apache Spark

Kafka

Relational databases

NoSQL databases

Cloud storage

Data lakes

Job description

Aquent Talent is hiring an AI Data Engineer 3 for an onsite role in Redmond, WA. The position focuses on building the data foundation for modern AI experiences, including synthetic data-generation systems, scalable pipelines, validation frameworks, and cloud-deployed backend services. You will work in a supportive environment with flexible work options and tools to thrive both personally and professionally.

What you’ll do

• Design, build, and evolve synthetic data-generation systems, evaluating existing approaches and trade-offs while creating scalable, maintainable workflows. Translate structured inputs, persona, and organizational context into coherent, traceable, and reproducible datasets.
• Build and operate scalable pipelines and orchestration, applying concurrency, asynchronous execution, and resilient failure handling where needed.
• Design and improve data models, storage strategies, and retrieval patterns to support large volumes and efficient access.
• Develop validation frameworks that define quality criteria and automate checks. Use quantitative analysis and profiling to assess data quality, investigate system behavior, and improve performance and reliability.
• Use cloud engineering practices to build, deploy, monitor, and support dependable systems, balancing scalability, operational simplicity, and cost.
• Evaluate technical trade-offs, communicate progress and risks, and collaborate on practical solutions. Maintain clear documentation, automated tests, and guidance that supports ongoing ownership.

What you’ll bring

• 5+ years of software or data engineering experience, or equivalent significant experience building systems with coding in languages and frameworks including, but not limited to, Python, Spring Boot, and .NET, with demonstrated ownership through implementation and support.
• Strong backend programming and systems engineering skills, including maintainable services, modular components, APIs, and automated tests.
• Deep experience designing, building, and operating data generation, ETL, and distributed data-processing workflows, including creating, scaling, and maintaining cloud-based pipelines.
• Strong working understanding of large language models (LLMs), including capabilities, limitations, common failure modes, and how data quality, prompting, evaluation, and model behavior influence AI-powered systems.
• Working knowledge of orchestration, concurrency, asynchronous execution, streaming or event-driven architectures, and recovery from failures.
• Strong knowledge of large-scale data storage, modeling, and retrieval, including partitioning, indexing, access patterns, and storage trade-offs.
• Strong quantitative analytical and troubleshooting skills to validate data and system behavior, interpret performance measurements, and make evidence-based engineering decisions.
• Practical cloud engineering experience and ability to work independently in an existing codebase.
• Clear technical communication, effective collaboration, and a disciplined approach to documentation and operational reliability.

Technologies you may work with

• Python, Spring Boot, .NET
• ETL, LLMs
• Apache Spark, Kafka
• Relational databases, NoSQL databases
• Cloud object storage, data lake architectures

What will set you apart

• Experience with Python.
• Experience integrating LLMs through AI platforms, frameworks, and APIs, including building or evaluating production-oriented AI workflows.
• Experience designing and running LLM output evaluations, including defining quality criteria and building automated checks.
• Experience with distributed processing, streaming or event-driven systems, and workflow orchestration tools such as Apache Spark, Kafka, or equivalent technologies, plus familiarity with relational or NoSQL databases, cloud object storage, or data lake architectures.
• Experience with observability, performance tuning, and deployment automation.
• Ability to work independently within an existing codebase and translate evolving requirements into maintainable solutions.
• Strong technical communication, documentation, collaboration, adaptability, and problem-solving skills.
• Experience creating automated tests and data-quality validation frameworks.

Compensation: USD 70 - 77 per hour. Experience requirement: 5+ years. Location: Redmond, WA (onsite).