SimplePractice

Data Engineer

• Partner with Product, Analytics and Engineering to build scalable systems that help unlock the value of data from a wide range of sources such as backend databases, event streams, and marketing platforms
• Lead technical vision and architecture with holistic point of view on both short-term and long-term horizons
• Work with analytics to create company wide alignment through standardized metrics across the company
• Work with Product and Engineering teams to support internal use cases such as financial reporting, product analytics and operational metrics
• Enable external use cases like customer-facing dashboard, self-serve analytics, and next best action in product
• Manage the complete data stack from ingestion through data consumption
• Build tools to increase transparency in reporting company wide business outcomes
• Work with DevOps to deploy and maintain data solutions leveraging cloud data technologies, preferably in AWS
• Help define data quality and data security framework to measure and monitor data quality across the enterprise. Define and promote data engineering best practice

Lead Data Engineer

Machine Learning Engineer

• Develop AI workflows, customize data pipelines, tune models, and engineer prompts to bring idea to prototype
• Work with subject matter experts to set up evaluation for AI workflows, ensuring rigor, quality and safety of content output
• Work with eng partners to integrate AI workflows into production
• Build and configure AI performance monitoring with proper reporting and alerts
• Optimize and maintain AI workflows for performance, reliability, and long-term scalability
• Conduct exploratory data analysis to answer key questions and test assumptions. Design experiments and build prototypes for proof-of-concept.
• Provide AI expert advice to product and eng partners in shaping product roadmap
• Partner closely with software eng, product, data eng, ML platform teams to scope and plan in execution
• Communicate timeline, milestones, findings w/ internal stakeholders
• Guide less experienced team members, sharing knowledge on LLM workflows and AI/ML model lifecycle
• Champion a culture of experimentation, continuous learning, and proactive problem-solving
• Stay current with emerging ML tools and technologies, integrating new techniques that elevate our product capabilities

Lead Data Engineer

Machine Learning Engineer, Applied AI