The ad says“Provisioning and monitoring of infrastructure for data systems, familiarity with IaC tools such as Terraform and Terragrunt”
The ad says“Build ingestion pipelines that process content from varied, inconsistent source formats — XML, JSON, PDF-derived, and API-delivered”
The ad says“4+ years of data engineering experience with strong Python skills — you’ve written parsers, validators, and transformation scripts for real-world messy data”
The ad says“Provisioning and monitoring of infrastructure for data systems, familiarity with IaC tools such as Terraform and Terragrunt”
The ad says“Provisioning and monitoring of infrastructure for data systems, familiarity with IaC tools such as Terraform and Terragrunt”
The ad says“Build ingestion pipelines that process content from varied, inconsistent source formats — XML, JSON, PDF-derived, and API-delivered”
Values
The ad says“Familiarity with agentic AI patterns or Model Context Protocol (MCP)”
The ad says“Familiarity with agentic AI patterns or Model Context Protocol (MCP)”
The ad says“Experience with vector databases (Pinecone, Weaviate, pgvector) or embedding pipeline tooling”
The ad says“Experience with vector databases (Pinecone, Weaviate, pgvector) or embedding pipeline tooling”
The ad says“Experience with vector databases (Pinecone, Weaviate, pgvector) or embedding pipeline tooling”
The ad says“Experience with vector databases (Pinecone, Weaviate, pgvector) or embedding pipeline tooling”
We’re looking for a Senior Data Engineer to join Kiddom’s Content & AI Systems team, building the data layer that powers next-generation AI-assisted curriculum authoring and content delivery
You’ll own the pipelines, schemas, and validation frameworks that turn messy, domain-specific curriculum content into structured, AI-ready data products, working closely with Instructional Designers, AI engineers, and the Content Agents team
This role suits an engineer who’s comfortable in a non-traditional data engineering space and energized by defining data requirements and infrastructure from the ground up
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
Benefits
Meaningful equity
Remote friendly culture with budget to make sure you can work comfortably from home or office
Health benefits: medical, dental, vision, disability and life insurance
Flexible vacation policy
Preferred experience
The data system operates, ECS, EKS clusters, provision lambdas and S3 buckets
Data quality mindset — you build validation and completeness checks in from the start, not as an afterthought
Provisioning and monitoring of infrastructure for data systems, familiarity with IaC tools such as Terraform and Terragrunt
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
Cross-functional collaborator — comfortable working with non-engineers to define requirements and translate domain knowledge into data structures
Background in education, curriculum design, or ed-tech — understanding how instructional content is authored and structured is a genuine differentiator
Familiarity with agentic AI patterns or Model Context Protocol (MCP)
Experience with vector databases (Pinecone, Weaviate, pgvector) or embedding pipeline tooling
