MTS – Data Engineering (AI)

TL;DR

MTS – Data Engineering (AI): Architecting and implementing data backbones for Copilot, focusing on building and optimizing high-scale ETL pipelines and experimentation frameworks. Focus on solving complex data challenges, ensuring data quality, and designing scalable data architectures for machine learning model training and inference.

Location: Redmond or San Francisco area, United States. Required to be in office 3 days a week. Starting January 26, 2026, employees within 50 miles of a Microsoft office are expected to work from that office at least four days a week.

Salary: USD $139,900 – $331,200 per year

Company

hirify.global is a global technology corporation focused on empowering individuals and organizations to achieve more through innovative AI solutions, including Copilot.

What you will do

• Build, maintain, and enhance data ETL pipelines for processing large-scale data with low latency and high throughput to support Copilot operations.
• Design and maintain high throughput, low latency experimentation reporting pipelines for measuring model performance and user engagement.
• Own data quality initiatives including monitoring, alerting, validation, and remediation processes.
• Implement robust schema management solutions for quick and seamless schema evolution.
• Develop and maintain data infrastructure supporting real-time and batch processing for machine learning model training and inference.
• Collaborate with ML engineers and data scientists to optimize data access patterns and improve pipeline performance.

Requirements

• Master’s Degree in Computer Science or related field AND 4+ years of experience in data engineering, OR Bachelor’s Degree AND 6+ years of experience.
• Experience building and maintaining production data pipelines at scale.
• Proficiency in writing production-quality Python, Scala, or Java code for data processing.
• Demonstrated experience with data quality frameworks and monitoring solutions.
• Ability to design scalable data architectures that handle growing data volumes.
• Strong collaboration skills with cross-functional teams to translate data requirements into technical solutions.

Nice to have

• Experience with technologies like Apache Spark, Kafka, or similar distributed processing frameworks.
• Experience building and scaling experimentation frameworks.
• Familiarity with cloud data platforms (Azure, AWS, or GCP) and their data services.
• Experience with data orchestration frameworks such as Airflow, Prefect, or Dagster.
• Experience with containerization technologies (Docker, Kubernetes) for data pipeline deployment.

Culture & Benefits

• Join a team dedicated to a growth mindset, innovation, and collaboration to achieve shared goals.
• Work in a culture of inclusion built on values of respect, integrity, and accountability.
• Opportunity to make a real impact on millions of users worldwide through world-class data products.