Senior ML Data Engineer - Enterprise Pipelines

Willsfly Technologies Inc; Texas; Hybrid; USD 135,000 - 170,000; Full time

Benefits offered by this job

Medical coverage

Performance incentive

Learning support

Certification reimbursement

Job summary

Willsfly Technologies Inc. is seeking an experienced ML Data Engineer in Texas, United States, to design and maintain data pipelines powering ML systems across enterprise software environments.

You will collaborate with AI, analytics, and platform teams to ensure reliable ingestion, feature flow, and scalable data readiness, while improving data quality and observability across production data layers. This role offers hybrid work and a strong emphasis on durable enterprise outcomes.

Qualifications

• 7+ years of experience in data engineering, ML data systems, or enterprise analytics environments.
• Strong knowledge of structured pipelines, transformation logic, and platform-scale data flow.
• Experience supporting model-ready or analytics-heavy product environments.
• Ability to work inside structured enterprise teams.

Responsibilities

• Design and maintain data pipelines that support ML systems and intelligent enterprise software environments.
• Support reliable data engineering across ingestion, feature flow, and scalable platform readiness.
• Collaborate with AI, analytics, and platform teams to improve model-ready enterprise data systems.
• Contribute to observability, quality, and performance across production-grade data layers.
• Improve maintainability and scale within enterprise ML data environments.

Skills

ML pipelines

Spark

Snowflake

Feature engineering

Data platform optimization

Education

Bachelor’s or Master’s degree in Data Engineering, Analytics, CS

Tools

Snowflake

Databricks

Job description

Willsfly Technologies Inc. is seeking an experienced ML Data Engineer in Texas, United States, to design and maintain data pipelines powering ML systems across enterprise software environments.

You will collaborate with AI, analytics, and platform teams to ensure reliable ingestion, feature flow, and scalable data readiness, while improving data quality and observability across production data layers. This role offers hybrid work and a strong emphasis on durable enterprise outcomes.