Data Operations Engineer

Personify Health provides personalized health, health plan administration, wellbeing, and care navigation solutions. The Data Operations Engineer supports healthcare and TPA claims data systems by maintaining and troubleshooting ETL/ELT pipelines, ensuring data accuracy across on-premises and cloud environments, and handling operational support tickets. The role collaborates with senior engineers, analysts, developers, and business users while developing skills in Python, SQL, and data workflows.

Responsibilities

Assist in maintaining and troubleshooting ETL/ELT data pipelines used for healthcare and TPA claims processing

Work with Python and SQL to support data extraction, transformation, validation, and loading across on-prem and cloud environments

Help monitor pipeline performance and resolve data issues, including reviewing logs, investigating failed jobs, and correcting data discrepancies

Handle support tickets (~50% of the time), responding to user requests, researching data questions, and helping resolve operational data problems

Supports daily process monitoring, including checking production processes and/or jobs, application performance, application maintenance, etc

Support data management tasks in systems such as PostgreSQL, Oracle, and cloud-based databases

Work with senior team members to learn data workflows, healthcare data formats, file transfers, and reporting processes

Assist in maintaining data quality and accuracy through routine checks, documentation, and follow-up

Collaborate with Data Analysts, Developers, and business users to understand data needs and support ongoing reporting and data operations

Participate in team meetings, sprint activities, and knowledge-sharing to continue developing data engineering skills

Preferred

Some exposure to cloud environments (AWS, Azure, ) is preferred but not required

Experience handling support tickets or operational data issues is strongly preferred (~50% of role)

Experience with healthcare EDI formats (e.g., 834, 835, 837, 999)

Exposure to AWS services (e.g., S3, RDS, Lambda) or cloud-based data tools

Experience with orchestration tools (Airflow), containers (Docker), or CI/CD pipelines

Familiarity with relational databases such as PostgreSQL, Oracle, or AWS RDS

Familiarity with AWS services (S3, RDS, Lambda, Glue) or modern data warehouses (Redshift, Snowflake)

Basic understanding of data modeling concepts (star/snowflake schemas, dimensional modeling)

Cloud environments AWS

Cloud environments Azure

Support ticket handling

Operational data issue handling

Healthcare EDI formats 834

Healthcare EDI formats 835

Healthcare EDI formats 837

Healthcare EDI formats 999

AWS services S3

AWS services RDS

AWS services Lambda

AWS services Glue

Cloud-based data tools

Airflow

Docker

CI/CD pipelines

Relational databases PostgreSQL

Relational databases Oracle

Relational databases RDS

Data warehouses Redshift

Data warehouses Snowflake

Data modeling

Star

snowflake schemas

Dimensional modeling

Benefits

Benefits effective day one

Comprehensive medical and dental through our own health solutions (yes, we use what we build)

Unlimited PTO—rest and recharge time is non-negotiable

Mental health support

Retirement planning

Financial protection

Professional development with clear career progression and learning budgets

Mission-driven culture where diverse perspectives drive real impact on people's health

Wellness programs

Company

Personalized health platform for plan administration, wellbeing, and navigation

Providence, Rhode Island, USA

https://personifyhealth.com/

Founded in 1994

1001-5000 employees

Qualifications

AWS Certification (or willingness to obtain within 6–12 months), such as **AWS Cloud Practitioner** or **AWS Developer – Associate**

2–3 years of experience in data engineering, analytics engineering, or a related technical role

Proficiency in Python and SQL, including writing queries, joins, basic transformations, and troubleshooting

Hands-on experience with relational databases (PostgreSQL, Oracle, AWS RDS) and familiarity with basic data warehouse concepts

Understanding of ETL/ELT pipelines, data validation, and data quality monitoring

Basic knowledge of **Linux command line** for navigating servers and running scripts

Familiarity with collaboration tools such as JIRA and Git/Bitbucket

Strong attention to detail and focus on data accuracy and reliability

Effective written and verbal communication skills; able to document findings and processes

Demonstrated ability to manage time, prioritize tasks, and follow through on commitments

Collaborative mindset, able to work with both technical and non-technical team members

Self-motivated, able to prioritize tasks, and eager to learn from senior team members

Interest in developing skills in ETL workflows, data pipelines, and cloud data environments over time

Some exposure to cloud environments (AWS, Azure, ) is preferred but not required

Experience handling support tickets or operational data issues is strongly preferred (~50% of role)

Experience with healthcare EDI formats (e.g., 834, 835, 837, 999)

Exposure to AWS services (e.g., S3, RDS, Lambda) or cloud-based data tools

Experience with orchestration tools (Airflow), containers (Docker), or CI/CD pipelines

Familiarity with relational databases such as PostgreSQL, Oracle, or AWS RDS

Familiarity with AWS services (S3, RDS, Lambda, Glue) or modern data warehouses (Redshift, Snowflake)

Basic understanding of data modeling concepts (star/snowflake schemas, dimensional modeling)

2–3 years of experience in data engineering, analytics engineering, or a related technical role.

Required skills

Python, SQL, ETL/ELT, PostgreSQL, Oracle, AWS RDS, AWS Cloud Practitioner, Linux Command Line, JIRA, Git/Bitbucket, Healthcare EDI 834, Healthcare EDI 835, Healthcare EDI 837, Healthcare EDI 999, Airflow

Experience

2+ years