Senior Data Engineer — Remote, Build Scalable Data Pipelines

Frederick Fox; Seattle (WA); Remote; USD 140,000 - 180,000; Full time

Job summary

Frederick Fox is hiring a Senior Data Engineer to own end-to-end delivery of large-scale batch and streaming data pipelines on AWS. You will help build and mature a data mesh that powers ML, analytics, and product engineering across the organization.

You will partner with Data Science, Product, and Engineering teams to onboard data products, optimize costs, performance, and reliability, and automate data operations including validation and recovery workflows.

Qualifications

• 5 years of hands-on data engineering experience building production pipelines.
• Deep, hands-on Apache Spark experience (Python/Scala) with tuning skills.
• Proven data solutions on AWS: EMR, Lambda, S3.
• Experience building both batch and streaming ingestion at scale.
• Strong data modeling and schema design for large datasets.
• Experience operating production pipelines: monitoring, alerting, incident response.
• Software fundamentals: version control, CI/CD, testing, code review.
• SQL proficiency for data analytics.

Responsibilities

• Own end-to-end delivery of large-scale batch and streaming data pipelines on AWS.
• Build and mature a data mesh powering ML, analytics, and product engineering.
• Collaborate with Data Science, Product, and Engineering to onboard data products.
• Optimize cost, performance, and reliability of data workloads.
• Automate data operations: validation, quality checks, alerts, backfills, recovery.
• Evaluate new technologies to keep the platform modern and efficient.

Skills

Spark Py/Scala

AWS EMR

Data modeling

SQL

CI/CD

Monitoring

Job description

Frederick Fox is hiring a Senior Data Engineer to own end-to-end delivery of large-scale batch and streaming data pipelines on AWS. You will help build and mature a data mesh that powers ML, analytics, and product engineering across the organization.

You will partner with Data Science, Product, and Engineering teams to onboard data products, optimize costs, performance, and reliability, and automate data operations including validation and recovery workflows.