Staff Data Engineer - Full Time

Experity; New York (NY); Remote; USD 141,000 - 190,000; Full time

Benefits offered by this job

Medical, Dental, Vision

Synthetic ownership program

Flexible work scheduling

Generous PTO

Learning program

Company events

401(k) with employer match

Quarterly bonuses

Job summary

Experity is seeking a Senior Data Engineer lead to architect and deliver scalable data pipelines and enterprise data services. You will define integration patterns across databases, APIs, and event streams, and design reusable data models for analytics and AI/ML applications.

You will mentor Senior Data Engineers, collaborate with Data Scientists, and drive improvements in Snowflake performance, cloud efficiency, and data quality.

Qualifications

• 8+ years of experience building and operating production-grade data platforms.

Responsibilities

• Lead the architecture and hands-on delivery of scalable batch and near-real-time ETL/ELT pipelines.
• Define robust integration patterns for databases, APIs, CDC/event streams and external sources.
• Design governed data models and data products for analytics, AI/ML and operations.
• Solve complex transformation challenges using Python, SQL, Snowflake and distributed data tech; produce production-ready code.
• Drive decisions on scalability, availability, resiliency, performance, maintainability and cost.

Skills

Python

SQL

Snowflake

Data pipelines

ETL/ELT

AWS

Leadership

Mentoring

Arch/Design

Education

Bachelor's or Master's in CS/Engineering/Info Systems

Tools

Kafka

Flink

Airflow

Docker

Kubernetes

Terraform

Git

Job description

About Experity

Experity is a mission-driven team transforming on-demand healthcare across the U.S., empowering urgent care clinics with industry-leading software that makes care faster, easier, and more patient-focused.

Joining us means doing meaningful work that directly improves the healthcare experience for millions—from helping families access care quickly to ensuring clinics run smoothly behind the scenes.

If you want to make a real impact alongside innovative, dedicated teammates while contributing to a trusted platform that’s becoming the operating system for on-demand care, Experity is the place to grow your career.

Benefits

• Comprehensive coverage starts first day of employment and includes Medical, Dental/Orthodontia, and Vision.Ownership


• All Team Members are eligible for synthetic ownership in Experity upon one year of employment with real financial rewards when the company is successful!


• This robust program includes counseling, legal resolution, financial education, pet adoption assistance, identity theft and fraud resolution, and so much more.


• Experity is committed to helping team members face the demands of juggling work, family and life-related issues by offering flexible work scheduling to manage your work-life balance.


• Experity offers a generous PTO plan and increases with milestones to ensure our Team Members have time to recharge, relax, and spend time with loved ones.


• Experity maintains a learning program foundation for the company that allows Team Members to explore their potential and achieve their career goals.


• 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.


• 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

• 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 featurepipelines, ML-ready datasets, and governed data access for Generative AI and agentic applications.


• Translate ambiguous product and business needs into practical architecture, phaseddelivery 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

This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.