About the role

Role Overview

Worth AI is hiring a Data Platform Engineer to design, build, and operate the core data services powering its products and analytics. The role treats the data platform as a product with strong SLAs, reliable self-service, and end-to-end ownership of pipelines and API services.

Responsibilities

Architect and implement entity resolution to deduplicate and link data points into unified “Golden Records.”
Design and maintain a high-performance global business knowledge graph and ontology (e.g., ownership chains, UBOs, and hidden risk relationships across borders).
Implement a hybrid storage strategy combining graph databases for relationships with document/search stores for metadata and adverse media content.
Optimize for real-time risk assessment with fast multi-level ownership traversal to support automated Go/No-Go onboarding decisions.
Build scalable data services and APIs for ingesting, transforming, and serving data to internal teams and external partners.
Develop and maintain batch and streaming pipelines using modern data processing frameworks and AWS tooling.
Ensure reliability and performance via monitoring, alerting, and (as appropriate) on-call.
Apply best practices for data modeling, quality, lineage, and governance.
Partner with data science, analytics, and application engineering to translate needs into platform capabilities.
Drive automation and standardization using CI/CD, model as a service, and reproducible environments.
Evolve data platform architecture with clear contracts, SLAs, and versioned APIs.

Requirements

Hands-on experience with graph databases (Neo4j, AWS Neptune, TigerGraph) and graph query languages (Cypher or Gremlin).
Proven experience with entity resolution / record linkage (e.g., Senzing, Quantexa, or custom probabilistic matching).
Ability to design flexible ontologies for evolving regulatory data.
Experience building GraphQL or REST APIs optimized for graph traversals and deep lookups.
Strong software engineering skills in one of: Python, Java, Go, Rust.
Hands-on AWS ETL/ELT and data pipeline development.
Familiarity with modern data stack tools: Spark/Flink, Kafka/Kinesis, Airflow (or managed schedulers), and warehouses such as Snowflake/Redshift/BigQuery/Databricks.
DevOps familiarity: CI/CD, Docker, Kubernetes, Terraform.
Strong observability focus (metrics/logs/traces), resilience, and early warning signals.

Nice-to-haves (implied by focus areas)

Deep experience building data platforms at scale (“data-as-a-service”).
Strong domain fit for risk/identity/ownership data and onboarding decisioning.

About Worth AI

Worth AI builds computer software products that use artificial intelligence to improve decision-making. The team focuses on collaboration and adaptability while delivering data-driven capabilities for internal users and external partners.