Senior Data Engineer - Scalable Pipelines & ML (Remote)

FutureFit AI; United States; Remote; USD 125,000 - 155,000; Full time

Job summary

FutureFit AI is seeking a Sr. Data Engineer to own the data foundation, building ingestion pipelines, data models, and analytics datasets for our AI-powered platform. You’ll shape how data is stored, tested, and consumed, and collaborate with engineering, product, and data science to deploy models in production.

You’ll work on a small, high-ownership team in a remote-friendly setup, using Python/SQL, Airflow, dbt, and cloud warehouses to enable reliable analytics and scalable ML features.

Qualifications

• 4+ years of production ETL/ELT pipelines design and operation.
• Fluency in Python and SQL with deep SQL for warehouse data modeling.
• Experience with orchestration stack (Airflow/dbt) and cloud data warehouses.
• Experience integrating data from varied sources (APIs, files) with schema changes.
• Comfort with large, messy data and judgment on cleaning vs modeling.
• Reliability mindset: testing, monitoring, and debugging pipelines.
• Clear communication of data models to non-technical audiences.

Responsibilities

• Design, build, and operate ingestion and transformation pipelines for labor market, customer, and product data into our warehouse and product.
• Own data modeling and quality for core data and taxonomies central to the product.
• Build datasets powering internal analytics and Looker/Quicksight reporting.
• Develop pipelines feeding matching and recommendation models and collaborate with engineers to deploy and monitor models in production.

Skills

Data engineering

Python

SQL

Airflow

dbt

Cloud data warehousing

Data modeling

Testing & monitoring

Communication

Tools

Airflow

dbt

PostgreSQL

Redshift

MongoDB

Looker

Quicksight

SageMaker

Job description

FutureFit AI is seeking a Sr. Data Engineer to own the data foundation, building ingestion pipelines, data models, and analytics datasets for our AI-powered platform. You’ll shape how data is stored, tested, and consumed, and collaborate with engineering, product, and data science to deploy models in production.

You’ll work on a small, high-ownership team in a remote-friendly setup, using Python/SQL, Airflow, dbt, and cloud warehouses to enable reliable analytics and scalable ML features.