HHiring Reality
← Machinify

Senior Data Engineer 

Machinify
Location
Remote - US
Posted
24 days ago
Department
Data Engineering
Salary
$180k-$220k
What they actually want (must-haves)
  • 6+ years of experience as a Data Engineer (or equivalent), building production-grade pipelines
  • Strong expertise in Python, Spark SQL, and Airflow
  • Experience processing large-scale file-based datasets (CSV, Parquet, JSON, etc.) in production environments
  • Experience mapping and standardizing raw external data into canonical models
  • Familiarity with AWS (or any cloud), including file storage and distributed compute concepts
  • Experience onboarding new customers and integrating external customer data with non-standard formats
Nice to have
  • Experience building or willingness to learn streaming pipelines using tools such as Kafka or SQS
  • Familiarity with healthcare data (837, 835, EHR, UB04, claims normalization)
What the job really is

As a Senior Data Engineer at Machinify, you will transform raw external healthcare data into actionable datasets that support payment and operational decisions. Your role involves designing and implementing production-grade data pipelines, onboarding new customers, and collaborating with various teams to ensure data accuracy and reliability. You will also be responsible for optimizing existing pipelines and contributing to the evolution of the data platform.

Benefits
  • Work from anywhere in the US
  • Full Medical/Dental/Vision for employees & their families
  • Flexible and trusting environment
  • Unlimited FTO
  • Competitive salary, equity, 401(k) including employer match
Things to weigh
  • Salary range is $180k-$220k, which may vary based on experience and qualifications
  • Role involves significant customer-facing responsibilities
  • Requires collaboration across multiple teams, which may add complexity to workflows
Job score4.2/5
Benefits3/5
Freshness4/5
Career value4/5
Role clarity5/5
Pay transparency5/5

Applying to Machinify?

See how your résumé matches this role — and tailor it from what actually gets interviews.