Senior Machine Learning Data Engineer - Feature Stores (AWS SageMaker)

Senior ML Data Engineer – Feature Stores (AWS SageMaker)

The Senior ML Data Engineer will design, build, and maintain scalable data and ML-ready data platforms, with a strong and mandatory focus on Feature Stores and machine-learning data pipelines. This role requires extensive, hands-on experience building feature stores in production, specifically using AWS SageMaker Feature Store (general SageMaker exposure alone is not sufficient).

The engineer will work across the full software development lifecycle, collaborating closely with Product, Data Science, and Analytics teams to enable reliable, reusable, and high-quality features for both training and real-time inference use cases.

Responsibilities

• Collaborate with Product and Data Science teams to gather requirements and translate them into scalable data and feature engineering solutions
• Design, build, and operate feature stores and feature pipelines, supporting both offline and online ML use cases
• Implement feature engineering workflows aligned with ML lifecycle best practices (training vs. inference parity, feature reuse, governance)
• Build and maintain robust data pipelines using AWS services
• Analyze requirements and propose appropriate technical architectures and solutions
• Develop, test, and deploy solutions using Python, SQL, and cloud-native tooling
• Ensure data quality, reliability, and performance across data lakes, warehouses, and feature stores
• Troubleshoot and resolve complex data, ETL, and SQL performance issues
• Apply out-of-the-box thinking to solve complex data and ML engineering challenges
• Work independently while contributing effectively in a collaborative, cross-functional environment
• Effectively prioritize and execute tasks in a fast-paced setting

Requirements

Must-Have Technical Skills (Non-Negotiable)

• Feature Store experience is mandatory:
• Direct, hands-on experience building and operating a feature store in production
• Experience with AWS SageMaker Feature Store specifically for feature store implementation, not just general SageMaker usage
• Experience with Tecton is highly desirable
• Expert-level SQL (this will be heavily evaluated)
• Strong Python scripting skills
• Strong hands-on experience with AWS services, including:
• AWS Lambda
• Amazon S3
• AWS Glue

Important Skills

• Proven experience designing and maintaining data pipelines
• Knowledge of CI/CD processes for data and ML workflows
• Data Warehousing experience:
• Snowflake preferred, or
• Amazon Redshift acceptable only if SQL expertise is exceptional
• Unix / shell scripting experience

Good-to-Have Skills

• Infrastructure as Code (IaC) experience (e.g., CloudFormation, Terraform)
• Strong understanding of data warehouse architecture
• Experience working closely with Data Science teams and understanding how features are consumed by ML models
• Familiarity with Informatica Intelligent Cloud Services (IICS) (not a priority)
• Prior experience using Snowflake

Hiring Process

Interview process typically consists of 3 rounds.

Why Join Us?

We offer a supportive and rewarding work environment with a range of benefits designed to help you thrive:

• Full-Time Position: Stability and growth in a dedicated role.
• Competitive Compensation: Get paid in US dollars, ensuring a strong and stable income.
• 100% Remote Work: Enjoy the flexibility of working from anywhere.
• Paid Time Off: Receive 12 PTO days per year to recharge and unwind.
• Local Holidays: Celebrate your country's holidays with paid time off.
• Birthday Off: Take your special day off, on us!
• Career Growth: Access clear career paths and opportunities for advancement.
• Recognition Program: Be celebrated for your achievements and contributions.
• Paid Leaves: Enjoy peace of mind with fully paid leaves.