Software Engineer Data Platform
Tools for Humanity is a technology company building products for humans in the age of AI. It contributes to World and develops proof-of-human hardware and software, including the Orb, alongside World App services for World ID and digital-asset management.
Maintainer signals as of 8/12/2026
Projects
About Tools for Humanity
Tools for Humanity is a technology company founded in 2019 and headquartered in San Francisco and Munich. It builds and operates services including World ID App and World App, which provide interfaces for creating and using World ID, accessing Mini Apps, and managing digital assets through a self-custodial wallet. It also develops the Orb and Orb Mini proof-of-human hardware and software, using encrypted iris-imaging processes and open-source components, and leads design and planning for World Spaces.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will maintain and evolve the data platform powering AI pipelines. You will design data quality and transformation workflows, build secure APIs and internal tooling, improve CI/CD and integration testing, and manage data assets, governance, lineage, access control, schemas, observability, and reliable event-driven pipelines in the Munich office.
Requirements
- 5+ years of proficiency in Python
- Strong system design fundamentals
- Experience designing and building secure, performant APIs
- Experience with Docker and Kubernetes
- Experience with AWS services and Terraform
- Experience designing and operating data ingestion and transformation workflows
- Exposure to Snowflake or other SQL-based analytics platforms
- Familiarity with CI/CD pipelines and version control
- Fundamentals in data modeling and schema design
- Experience with MongoDB
- Knowledge of data partitioning and large-scale dataset optimization
- Experience with event-driven pipelines using SQS, SNS, Lambda, or Step Functions
Responsibilities
- Design and operate automated data quality pipelines
- Develop transformation processes for analytics and model training datasets
- Instrument systems with metrics, alerts, and recovery mechanisms
- Build internal tooling and dashboards
- Build secure APIs and backend services for large datasets
- Improve CI/CD pipelines, integration tests, and dependency management
- Own data asset lineage, access control, and schema enforcement
- Handle structured and semi-structured data
- Build resilient pipelines with versioning and schema evolution
