Data Engineer II

Personify Health•Tempe, AZ

About The Position

Ready to build the data infrastructure that keeps healthcare claims moving? Every claim, eligibility check, and provider record that flows through our systems depends on pipelines that work — every time, without exception. As a Data Engineer II, you're the person who builds and maintains that backbone, turning raw healthcare and TPA data into clean, reliable information that analysts, business teams, and ultimately our clients and members can count on. When a pipeline breaks or data goes bad, real people feel it — a claim gets delayed, a report goes out wrong, a decision gets made on bad numbers. Your work closing that gap between messy source data and trustworthy systems is what lets the rest of the organization move fast with confidence. Get the pipelines right, and you're not just moving data — you're moving outcomes for the members and clients who rely on us.

Requirements

Bachelor's degree in computer science, information systems, or a related field, or equivalent experience
3+ years in data engineering or analytics engineering, ideally within TPA, healthcare, insurance, or claims processing
1+ years in system/data analysis or process improvement
1+ years in the healthcare industry preferred
Proficient in Python and SQL, including complex queries (pivots, window functions, date calculations)
Hands-on experience with orchestration tools (Airflow), containers (Docker), and CI/CD pipelines
Exposure to data files like OMS or HL7, or ability to parse delimited files into a database using Python scripting
Familiarity with REST APIs, JSON, and non-relational data models, including converting that data into relational models
Experience with JIRA, BitBucket Git, and BitBucket Pipelines
Proficient in Excel; familiar with analytical tools like Tableau, Power BI, or MicroStrategy
Solid understanding of data modeling concepts (star/snowflake schemas, dimensional modeling) and relational vs. non-relational models

Nice To Haves

AWS Certification preferred: AWS Certified Cloud Practitioner, Developer – Associate, or Data Engineer – Associate
Exposure to healthcare EDI transactions (834, 835, 837, 2222, 2223, 999) preferred
Working knowledge of AWS services (S3, Glue, EC2, MWAA, Lambda, ECS) a plus; Infrastructure as Code (Terraform) a plus
Experience with relational databases (PostgreSQL, Oracle, AWS RDS); exposure to modern data warehouses (Snowflake, Redshift) a plus

Responsibilities

Build ETL/ELT pipelines: Design and maintain pipelines that ingest and transform healthcare and TPA data — including claims, provider, and eligibility sources — turning raw feeds into usable, trustworthy data sets.
Orchestrate and monitor workflows: Develop, schedule, and monitor pipelines using Airflow, CloudWatch, ECS, and DAGs, following established CI/CD, observability, and governance practices to keep data flowing reliably.
Develop data applications: Build workflows and applications using Python, SQL, and Django, working across PostgreSQL, Oracle, and cloud-native databases to power downstream reporting and analysis.
Support core healthcare data processes: Manage EDI file transfers, claims adjudication support, audits, and reporting workflows that keep compliance and operations on track.
Own data extraction and documentation: Extract, cleanse, and load new data sets, investigating and documenting source systems so the team always knows what it's working with.
Translate requirements into solutions: Partner with Data Analysts, Data Scientists, Product, Reporting, and Account Management to turn business needs into working data pipelines.
Safeguard data quality and compliance: Implement quality assurance rules and automated validation that keep data accurate, complete, and secure under HIPAA and CMS regulations.
Troubleshoot and resolve pipeline issues: Monitor pipeline health, triage incoming bugs and incidents, and resolve data flow issues — escalating complex architectural problems to senior engineers when needed.
Contribute to data modeling decisions: Apply data modeling patterns and standards set by senior engineers, contributing your perspective to pipeline design discussions.
Guide junior engineers: Help onboard and mentor Data Engineer I team members, sharing what you learn so the whole team levels up.
Automate routine work: Build automation for repetitive operational workflows and reporting, cutting out manual busywork wherever possible.