New Grad Data Engineer (for Health Tech Startup)🤓

1Phi Health is a health tech startup focused on making healthcare more accessible. They are seeking a New Grad Data Engineer to build and maintain data pipelines, ensuring data quality and collaborating with data scientists and product engineers in the healthcare data domain.

Responsibilities:

• Build and maintain data pipelines that ingest, transform, and validate large-scale Medicare claims data using SQL, Python, and Databricks (Spark). You'll work with patient-level records across billions of claim lines
• Write and optimize complex SQL — multi-step transformations, window functions, joins across large datasets, aggregations with suppression rules. SQL is the primary language of the work
• Automate and operationalize recurring data workflows — building reliable, repeatable pipelines that process CMS data extracts, dimension tables, and derived provider metrics
• Ensure data quality by designing validation checks, reconciling source data against expected schemas, and investigating anomalies when numbers don't add up
• Collaborate with data scientists and product engineers to define output schemas, deliver clean datasets, and support downstream analytics and application features
• Work in cloud infrastructure — primarily Databricks on AWS, with exposure to S3, Unity Catalog, and related services
• Learn the healthcare data domain — you'll develop working knowledge of claims data structures, medical coding systems (ICD-10, HCPCS, DRG), and CMS data programs

Requirements:

• Build and maintain data pipelines that ingest, transform, and validate large-scale Medicare claims data using SQL, Python, and Databricks (Spark). You'll work with patient-level records across billions of claim lines
• Write and optimize complex SQL — multi-step transformations, window functions, joins across large datasets, aggregations with suppression rules. SQL is the primary language of the work
• Automate and operationalize recurring data workflows — building reliable, repeatable pipelines that process CMS data extracts, dimension tables, and derived provider metrics
• Ensure data quality by designing validation checks, reconciling source data against expected schemas, and investigating anomalies when numbers don't add up
• Collaborate with data scientists and product engineers to define output schemas, deliver clean datasets, and support downstream analytics and application features
• Work in cloud infrastructure — primarily Databricks on AWS, with exposure to S3, Unity Catalog, and related services
• Learn the healthcare data domain — you'll develop working knowledge of claims data structures, medical coding systems (ICD-10, HCPCS, DRG), and CMS data programs
• You have strong SQL skills. Coursework, internships, or projects where you wrote non-trivial queries — joins, CTEs, window functions, aggregations. You can reason about query performance
• You're comfortable with Python. You've used it for data manipulation (pandas, PySpark, or similar). You don't need to be a software engineer, but you can write clean, functional code
• You understand data pipeline concepts — ETL/ELT, idempotency, schema management, data validation. Exposure through coursework, capstone projects, or internships counts
• You're detail-oriented and methodical. Healthcare data has strict rules around suppression, privacy, and accuracy. You care about getting the numbers right
• You're a fast learner who's comfortable ramping up on unfamiliar domains. You'll be learning Medicare claims data, CMS programs, and healthcare coding systems on the job
• You have a BS or MS in Computer Science, Data Science, Information Systems, Statistics, or a related field
• You've worked with Spark, Databricks, or other distributed compute environments (even in a class or personal project)
• You have exposure to cloud platforms (AWS, GCP, or Azure) — S3, IAM, or managed database services
• You've touched healthcare data in any capacity — claims, EHR, public health datasets, MIMIC, CMS public use files
• You're familiar with version control (Git) and collaborative development workflows
• You've built a data project end-to-end — ingestion through delivery — even if it was small