Senior Engineer (Data Operations)

Senior Engineer (Data Stack Focused)

at Rialtic, Inc.

Atlanta or Remote

About Rialtic

Rialtic is an enterprise software platform empowering health insurers and healthcare providers to run their most critical business functions. Founded in 2020 and backed by leading investors including Oak HC/FT, F-Prime Capital, Health Velocity Capital and Noro-Moseley Partners, Rialtic's best-in-class payment accuracy product brings programs in-house and helps health insurance companies gain total control over processes that have been managed by disparate and misaligned vendors. Currently working with leading healthcare insurers and providers, we are tackling a $1 trillion problem to reduce costs, increase efficiency and improve quality of care. For more information, please visit www.rialtic.io.

The Role:

We’re looking for a data-stack-focused engineer to join our core platform team. If you’re excited by the chance to deal with “big data,” healthcare is the place to be. Rialtic works with the largest healthcare organizations in the United States. Our goal is to improve the healthcare revenue cycle and reduce administrative waste, making the system more efficient for everyone. We’re built on a modern, cloud-first stack, but our clients are often on legacy systems, so the challenges of data extraction, data mapping, and data processing are significant… but there’s a huge opportunity to advance the state of the art. 

We tackle challenges that are common to healthcare companies and healthcare data. Interface and interoperability standards exist (e.g. X12 EDI / HL7 / FHIR) but nobody really follows them to the letter. Many of our interfaces involve legacy systems that predate those standards. While we create templates and establish best practices, every implementation is unique in some way, as we must adapt to the business processes and underlying assumptions of each client. We often fix inconsistencies in the data we receive, or have to make determinations about which information is the most current or relevant across disparate systems over time. Our data pipelines run 24x7 with a mixture of batch and near real-time / API-driven endpoints. You can’t work with PHI in lower environments, but properly de-identifying data removes a lot of the information that is needed to do accurate analysis (try writing a measure that is sensitive to the date of service and the age of the patient when the only data you’re allowed to use has had everything stripped except for the year of the event and the patient’s age can’t be specified numerically because they are elderly and live in a sparsely populated area … and oh, by the way, we had to truncate the ZIP code too). Our ability to parse, validate, process, write code against, and manage enormous volumes of data while performing complex analyses quickly and accurately is critical to our success. 

If that sounds like a fun challenge, then you should apply for this position!

During any given week in this role, you might:

• Work with clients and prospective clients to define and implement a data mapping strategy for healthcare claims and related data (both initial/historical data loads and ongoing data flows);
• Write and test pipeline components, DAGs, and documentation for ETL/ELT, data validation, observability, and error reporting;
• Partner with our cloud/SRE team to understand the performance characteristics and storage requirements for our data lake, data warehouses, and in/outbound file storage;
• Assist our infosec team in documenting the provenance and classification of data sets and metadata, including our HIPAA-compliant data de-identification strategy and process;
• Implement and test improvements to slow-running queries, refactor and propose schema changes, migrations, and entirely new tables/data stores for our transactional, operational, and analytical data; 
• Participate with internal and external stakeholders to understand the business logic and other requirements (such as refresh latency) for our Web-based payment integrity solution, client data warehouse exports, and one-time/ad-hoc analysis needs;
• Pilot a new tool (either something you helped build in SQL, Python, Go, or other languages, or a modern data stack tool from an open-source project or a third-party vendor) to help improve the automation and reliability of our data processing infrastructure; 
• Serve as a peer reviewer for a colleague’s code, participate in an engineering architecture specification review, work with the product management team to refine a set of requirements or break a story down into concrete tasks for implementation.
• Monitor and manage ongoing batch and real-time data operations and troubleshoot issues for clients that include some of the largest healthcare organizations in