Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field.
8+ years of experience in building and operating production-grade data platforms.
Expert proficiency in Python and SQL with strong software design and testing practices.
Deep hands-on experience with Snowflake, including data modeling and performance optimization.
Strong experience with AWS services like S3, Lambda, and Kinesis.
Proven ability to lead technical decisions and mentor engineers across teams.
Experience with CI/CD, Git workflows, and Infrastructure as Code practices.
Responsibilities
Lead the architecture and delivery of scalable ETL/ELT pipelines and data platform services.
Define integration patterns for databases, APIs, and external data sources.
Design reusable data models and curated data products for analytics and AI/ML applications.
Solve complex data transformation challenges using Python, SQL, and distributed technologies.
Drive architectural decisions for scalability, performance, and cost efficiency.
Establish observability and automated recovery patterns for critical data pipelines.
Mentor engineers and communicate architectural decisions to stakeholders.
Benefits
Opportunity to work remotely or in an office setting.
Full Job Description
Compensation: Budgeted between $140,750 and $190,000 dependent upon applicable experience.
Remote: Experity offers Team Members the opportunity to work remotely or in an office.
Responsibilities:
Data Engineering & Platform
Lead the architecture and hands-on delivery of scalable batch and near-real-time ETL/ELT pipelines and data platform services.
Define robust integration patterns for databases, APIs, applications, CDC/event streams, and external sources across Experity's enterprise data platform.
Design reusable, governed data models, curated data products, and semantic-ready datasets for analytics, reporting, AI/ML, and operational applications.
Solve complex transformation and processing challenges using Python, SQL, Snowflake, and distributed data technologies; contribute production-quality code.
Drive architectural decisions and tradeoffs for scalability, availability, resiliency, performance, maintainability, and cost efficiency.
Engineering Excellence & DataOps
Define reusable frameworks, libraries, reference architectures, and engineering patterns adopted across data engineering teams.
Advance automated testing, CI/CD, Infrastructure as Code, and DataOps standards; improve developer workflows using AI-assisted engineering tools with appropriate review and controls.
Establish observability, data freshness, service-level objectives, alerting, and automated recovery patterns for critical data pipelines.
Lead resolution of high-impact production issues, cross-system root-cause analysis, and preventive architectural improvements.
Drive measurable improvements in Snowflake and cloud compute efficiency, query performance, storage, and platform operating costs.
Data Quality, Governance & Security
Set technical standards for data quality, validation, lineage, reconciliation, and trusted data delivery across critical domains.
Partner with Data Architecture, Governance, and Security teams to design discoverable, governed, reusable data assets and consistent access patterns.
Design healthcare data solutions with privacy, security, and applicable compliance requirements embedded from the outset.
Technical Leadership & Collaboration
Serve as a technical authority for cross-team design reviews, architecture decisions, code quality, and engineering best practices.
Mentor Senior and other Data Engineers through design guidance, pairing, code reviews, and technical knowledge sharing; lead through influence rather than direct reports.
Partner with Data Scientists and Machine Learning Engineers on reliable feature
pipelines, ML-ready datasets, and governed data access for Generative AI and agentic applications.
Translate ambiguous product and business needs into practical architecture, phased
delivery plans, and measurable technical outcomes.
Communicate architectural decisions, tradeoffs, risks, and operational practices clearly to engineering and non-engineering stakeholders.
Education and Experience:
Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field.
8+ years of experience building and operating production-grade data platforms, including demonstrated ownership of complex systems or cross-team technical initiatives.
Expert proficiency in Python and SQL, with strong software design, testing, debugging,and code review practices.
Deep hands-on experience with Snowflake, including data modeling, query optimization, performance, and cost management.
Experience architecting CDC, Kafka, Flink, or comparable distributed event and streaming solutions.
Strong experience with AWS, including S3, Lambda, Glue, Kinesis, or equivalent cloud services.
Proven experience designing high-volume batch and near-real-time ETL/ELT architectures and reusable analytical data models.
Experience with orchestration technologies such as Airflow or equivalent platforms.
Experience establishing CI/CD, Git-based workflows, automated testing, and Infrastructure as Code practices.
Strong command of data quality, governance, security, observability, production operations, and reliability engineering.
Demonstrated ability to lead technical decisions across teams, mentor engineers, and communicate tradeoffs effectively.
A hands-on, full-stack mindset and willingness to own complex problems end-to-end, from design through production operations.
Preferred:
Experience with dbt and dimensional modeling or semantic-layer design.
Experience using AI-assisted development tools responsibly to improve engineering velocity, code quality, testing, and operational efficiency.
Experience supporting Machine Learning, Generative AI, feature engineering, and AIready data access patterns.
Experience with Docker, Kubernetes, Terraform, or CloudFormation.
Experience with metadata management, data catalogs, lineage, data observability, and data product governance.
Experience in Healthcare, HealthTech, SaaS, or other regulated industries.
About Experity
Experity is a healthcare software company based in Buffalo Grove, Illinois. The company provides a range of software solutions for urgent care clinics, including electronic medical records, practice management, and revenue cycle management. Experity's software is designed to help urgent care clinics improve patient care, increase efficiency, and reduce costs. The company was founded in 2019 through the merger of DocuTAP and Practice Velocity.
