Career Development – Experity maintains a learning program foundation for the company that allows Team Members to explore their potential and achieve their career goals.
Team Building – We bring our Team Members together when we can to strengthen the team, build relationships, and have fun! We even have a family company picnic and a holiday party.
Total Compensation - Competitive pay, quarterly bonuses and a 401(k) retirement plan with an employer match to help you save for your future and ensure that you can retire with financial security.
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.
Equal Opportunity Employer
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