Sr. Engineer, Data (AI, Data Engineering)

TL;DR

Sr. Engineer, Data (AI, Data Engineering): Building scalable, reusable data pipelines and platforms that enable analytics, operational reporting, and platform modernization with an accent on data quality monitoring, enterprise architecture patterns, and generative AI-assisted engineering. Focus on designing and improving event-driven, distributed data solutions and delivering production-ready pipelines through testing, telemetry, and code reviews.

Location: Remote - Nationwide

Salary: $101,600 - 175,200

Company

hirify.global provides technology-enabled revenue cycle management solutions for health systems and physician groups.

What you will do

• Develop, test, deploy, monitor, and continuously improve scalable data pipelines and API integrations.
• Apply generative AI tools in day-to-day engineering to accelerate development, improve code quality, troubleshoot complex systems, and design scalable data solutions.
• Translate product and business concepts into incremental, high-quality technical deliverables.
• Design solutions through hands-on sessions and influence standards via thorough code reviews.
• Implement data quality monitoring and recommend architectural patterns aligned with enterprise platform best practices.

Requirements

• 6+ years of hands-on experience developing solutions using Microsoft SQL.
• 3+ years with big data technologies including Databricks, Apache Spark, Python, and Microsoft Azure (ADF, Dataflows, Azure Functions, Service Bus).
• Strong engineering fundamentals: automated testing, code reviews, telemetry, iterative delivery, and DevOps.
• Experience with polyglot storage architectures (relational, columnar, key-value, graph).
• Hands-on experience with Delta Lake tables and Parquet data stored in ADLS.
• Ability to communicate effectively with technical and non-technical, globally distributed stakeholders.

Nice to have

• Experience working with healthcare datasets; familiarity with HL7 or EDI.

Culture & Benefits

• Comprehensive benefits package including healthcare, time off, retirement, and well-being programs.
• Professional development support with certifications and tuition reimbursement.
• Quarterly and annual incentive programs for employees who exceed expectations.
• Emphasis on collaboration, growth, and innovation.

Hiring process

• Interview process includes technical evaluation focused on data engineering and architecture.
• Code review/design discussions to assess engineering standards and collaboration.