Senior Data Engineer

Kiddom is redefining how technology powers learning. We combine world-class curriculum with cutting-edge AI and modern SaaS infrastructure to help schools deliver truly personalized education at scale. Our platform equips educators with real-time insights and flexible tools, enabling them to focus on what matters most—driving student growth and equity. We’re not just building technology; we’re driving innovation in an industry ready for transformation. At Kiddom, team members sit at the center of this effort, collaborating across engineering, design, research, and education to create experiences that push boundaries and unlock new possibilities for learners and educators alike. If you thrive in ambiguity, love working in high-ownership cultures, and are energized by the intersection of human impact and next-gen technology, this is the place to shape something transformative. Kiddom’s Content & AI Systems team is building the data layer that powers the next generation of AI-assisted curriculum authoring and content delivery. This role sits at the intersection of data engineering and content systems — owning the pipelines, schemas, and validation frameworks that turn raw curriculum content into structured, AI-ready data products.  This is not a traditional data engineering role. Curriculum content is messy, inconsistent, and deeply domain-specific. You will work closely with Instructional Designers, AI engineers, and the Content Agents team to define data requirements, design schemas, and build the infrastructure that makes AI-powered authoring workflows possible. Salary range is dependent on geographic location, prior experience, seniority, and demonstrated role related ability during the interview process. What we offer: Full time permanent employees are eligible for the following benefits from their first day of employment: * Competitive salary * Meaningful equity * Health insurance benefits: medical (various PPO/HMO/HSA plans), dental, vision, disability and life insurance * One Medical membership (in participating locations) * Flexible vacation time policy (subject to internal approval). Average use 4 weeks off per year. * 10 paid sick days per year (pro rated depending on start date) * Paid holidays * Paid bereavement leave * Paid family leave after birth/adoption. Minimum of 16 paid weeks for birthing parents, 10 weeks for caretaker parents. Meant to supplement benefits offered by State. * Commuter and FSA plans Equal Employment Opportunity Policy Kiddom is committed to providing equal employment opportunities to all employees and applicants without regard to race, religion, color, gender, sexual orientation, transgender status, national origin, citizenship status, uniform service member status, pregnancy, age, genetic information, disability, or any other protected status in accordance with all applicable federal, state, and local laws. country: US all locations: [San Francisco] commitment: Full-time department: Engineering location: San Francisco team: Data Science & Machine Learning You will...: Design and own the schema and data models representing Kiddom’s curriculum content (lessons, activities, standards alignments) for downstream use  Build ingestion pipelines that process content from varied, inconsistent source formats — XML, JSON, PDF-derived, and API-delivered Develop Python-based parsers, transformers, and validation scripts that enforce schema conformance and content quality at scale  Collaborate directly with Instructional Designers and product teams to translate content authoring workflows into data engineering requirements Build and maintain embedding and vector database pipelines that feed Kiddom’s AI-powered content features as they scale Work in Git-based workflows — treating data artifacts with the same rigor as software: versioned, reviewed, and documented What we're looking for...: 4+ years of data engineering experience with strong Python skills — you’ve written parsers, validators, and transformation scripts for real-world messy data Schema design instincts — you think carefully about how data should be structured for downstream use, not just how to move it  Data quality mindset — you build validation and completeness checks in from the start, not as an afterthought  Cross-functional collaborator — comfortable working with non-engineers to define requirements and translate domain knowledge into data structures Provisioning and monitoring of infrastructure for data systems, familiarity with IaC tools such as Terraform and Terragrunt The data system operates, ECS, EKS clusters, provision lambdas and S3 buckets Bonus: : Background in education, curriculum design, or ed-tech — understanding how instructional content is authored and structured is a genuine differentiator Experience with vector databases (Pinecone, Weaviate, pgvector) or embedding pipeline tooling  Familiarity with agentic AI patterns or Model Context Protocol (MCP)