Data Engineer IV

Planet DDS is on a mission to revolutionize dental software with their modern, AI-powered technology. The Data Engineer IV will lead the design and development of the company's data and analytics platform, focusing on building scalable and reliable solutions that drive smarter decision-making across the organization.

Responsibilities:

• Architect and build AI-ready data pipelines and ELT workflows with automation at the core, enabling real-time analytics, ML model serving, and generative AI applications across product and operations
• Lead the design of a modern data platform built for AI workloads, including vector stores, feature stores, and infrastructure that supports LLM integration and AI-powered analytics
• Partner with Product, Engineering, and Data Science teams to define data models and ensure high-quality, reliable data delivery across the organization
• Define and enforce data and software engineering best practices, including version control, data quality standards, documentation, and testing frameworks
• Mentor and coach junior and mid-level data engineers, fostering a culture of technical excellence and continuous growth
• Evaluate, adopt, and champion new data technologies and frameworks to drive platform modernization and efficiency
• Collaborate with senior leadership to define the long-term data strategy and roadmap aligned with company objectives
• Lead complex, cross-functional data initiatives from scoping through delivery, ensuring stakeholder alignment and on-time execution
• Optimize data infrastructure for performance, cost efficiency, and reliability at scale
• Ensure data security, compliance, and governance across the data platform, including adherence to HIPAA and other applicable regulations
• Champion AI tooling adoption across the data team, including AI-assisted development, automated data quality monitoring, anomaly detection, and intelligent pipeline observability
• Design and maintain data products that serve AI/ML models in production, including feature engineering pipelines, model input/output logging, and feedback loop infrastructure

Requirements:

• 8-11+ years of experience in data engineering, with demonstrated progression to senior or staff-level responsibilities
• Expert-level proficiency in SQL and at least one programming language (Python, Scala, or Java)
• Deep experience designing and building data pipelines using tools such as Apache Spark, Airflow, dbt, or equivalent
• Proven ability to design and implement cloud-based data platforms (AWS, Azure, or GCP), including data lake and warehouse solutions (Databricks, Snowflake, Redshift, BigQuery, or similar) and AI/ML platforms
• Strong understanding of data modeling, schema design, and database performance optimization for large-scale datasets
• Experience with real-time and streaming data architectures (Kafka, Kinesis, or equivalent)
• Hands-on experience integrating AI/ML workflows into data platforms, including feature stores, vector databases, or LLM-adjacent data pipelines (e.g., RAG systems, embedding pipelines)
• Familiarity with AI development tooling and a demonstrated habit of using AI to accelerate engineering work, code review, documentation, and data modeling
• Demonstrated ability to lead technical projects and influence architectural decisions across teams and functions
• Excellent communication skills with the ability to translate complex technical concepts for non-technical stakeholders
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent professional experience
• Experience with data governance, data quality frameworks, and compliance requirements; familiarity with HIPAA is strongly preferred