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Machinify

AI-driven healthcare operations optimization platform

Senior Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • At least 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 such as CSV, Parquet, and JSON in production environments.
  • Experience mapping and standardizing raw external data into canonical models.
  • Familiarity with AWS or another cloud, including file storage and distributed compute concepts.
  • Experience onboarding new customers and integrating external customer data with non-standard formats.
  • Ability to work across teams, manage priorities, and own complex data workflows with minimal supervision.
  • Strong written and verbal communication skills to explain technical concepts to non-engineering partners.
  • Comfort designing pipelines from scratch and improving existing pipelines.
  • Experience working with large-scale or messy datasets, such as healthcare, financial, or log data.
  • Experience building or willingness to learn streaming pipelines using tools such as Kafka or SQS.

Responsibilities

  • Design and implement robust, production-grade pipelines using Python, Spark SQL, and Airflow to process high-volume file-based datasets, including CSV, Parquet, and JSON.
  • Lead efforts to canonicalize raw healthcare data, including 837 claims, EHR, partner data, and flat files, into internal models.
  • Own the lifecycle of core pipelines, from file ingestion to validated, queryable datasets, ensuring high reliability and performance.
  • Onboard new customers by integrating their raw data into internal pipelines and canonical models; collaborate with subject matter experts, Account Managers, and Product to ensure successful implementation and troubleshooting.
  • Build resilient, idempotent transformation logic with data quality checks, validation layers, and observability.
  • Refactor and scale existing pipelines to meet growing data and business needs.
  • Tune Spark jobs and optimize distributed processing performance.
  • Implement schema enforcement and versioning aligned with internal data standards.
  • Collaborate with Data Analysts, Data Scientists, Product Managers, Engineering, Platform, subject matter experts, and Account Managers to ensure pipelines meet evolving business needs.
  • Monitor pipeline health, participate in on-call rotations, and proactively debug and resolve production data flow issues.
  • Contribute to the evolution of the data platform by driving mature patterns in observability, testing, and automation.
  • Build and enhance streaming pipelines using Kafka, SQS, or similar tools where needed to support near-real-time data needs.
  • Help develop and champion internal best practices around pipeline development and data modeling.

Desired Qualifications

  • Familiarity with healthcare data, including 837, 835, EHR, UB04, and claims normalization.

About the Company

Machinify

AI-driven healthcare operations optimization platform

Machinify uses artificial intelligence to optimize decision-making in healthcare. It provides AI-driven applications that improve operational efficiency for health plans, payers, and providers, acting as a central nervous system to manage data-driven decisions. Its products streamline processes like claims processing and utilization management by deploying a growing portfolio of applications on the Machinify platform, designed for fast implementation and immediate ROI. Differences from competitors include an AI-first platform tailored to healthcare administration, a rapid deployment model, and a customer-focused approach that aims to deliver tangible results quickly. The company’s goal is to transform healthcare administration by reclaiming control over healthcare expenditures and improving efficiency and patient outcomes through data-driven decisions.

Company size
1,001-5,000
Company stage
Series A
Total funding
$15.6M
Headquarters
Palo Alto, CA
Founded
2016

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