Worth AI, a leader in the computer software industry, is looking for a talented and experienced Data Platform Engineer to join their innovative team. At Worth AI, we are on a mission to revolutionize decision-making with the power of artificial intelligence while fostering an environment of collaboration, and adaptability, aiming to make a meaningful impact in the tech landscape.. Our team values include extreme ownership, one team and creating reaving fans both for our employees and customers.
As a Data Platform Engineer, you will design, build, and operate the core data services that power our products and analytics. You’ll own end-to-end data pipelines and API services that ingest, process, and expose high-quality data to internal customers (data science, analytics, product, and other engineering teams) and external partners.
You’ll be part of a small, high-impact team that treats the data platform as a product with strong SLAs, and reliable self-service for internal and external users.
Responsibilities
What you’ll do:
- Architect and implement entity resolution logic to de-duplicate and link disparate data points into unified "Golden Records" for businesses and individuals
- Design and maintain a high-performance global business knowledge graph and ontology to map complex ownership chains, UBOs, and hidden risk relationships across international borders
- Implement a hybrid storage strategy that bridges graph databases for relationship mapping with document and search stores for rich metadata and adverse media content
- Optimize the platform for real-time risk assessment, ensuring the ability to traverse multiple levels of ownership in milliseconds to support automated "Go/No-Go" onboarding decisions
- Design and build scalable data services and APIs for ingesting, transforming, and serving data across the company
- Develop and maintain batch and streaming data pipelines using modern data processing frameworks and AWS cloud-native tooling
- Own the reliability, performance, and API first data platform, including monitoring, alerting, and on-call where appropriate
- Implement best practices for data modeling, quality, lineage, and governance to ensure trustworthy, well-documented datasets
- Work closely with data scientists, analysts, and application engineers to understand their needs and translate them into robust platform capabilities
- Drive automation and standardization through CI/CD, model as a service, and reproducible environments
- Help define and evolve the architecture of our data platform as a true internal service with clear contracts, SLAs, and versioned APIs
