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.
