Data Platform Engineer

Role Overview

Worth AI is hiring a Data Platform Engineer to design, build, and operate the core data services that power product and analytics. You’ll own end-to-end data pipelines and API services that ingest, process, and expose high-quality data to internal teams and external partners, treating the data platform as a product with strong SLAs.

Responsibilities

• Architect and implement entity resolution to de-duplicate and link data into unified "Golden Records".
• Design and maintain a high-performance business knowledge graph and ontology for ownership chains, UBOs, and hidden risk relationships.
• Build a hybrid storage strategy combining graph databases with document/search stores for rich metadata and adverse media content.
• Optimize for real-time risk assessment, enabling multi-level ownership traversal in milliseconds for automated "Go/No-Go" onboarding decisions.
• Develop scalable data services and APIs to ingest, transform, and serve data across the company.
• Build and maintain batch and streaming pipelines using modern data processing frameworks and AWS tooling.
• Own reliability, performance, and API-first data platform operations (monitoring, alerting, and on-call as needed).
• Apply best practices for data modeling, quality, lineage, and governance.
• Partner with data scientists, analysts, and application engineers to translate needs into platform capabilities.
• Drive automation and standardization via CI/CD, “model as a service”, and reproducible environments.
• Help evolve the data platform architecture with clear contracts, SLAs, and versioned APIs.

Requirements

• Hands-on experience with graph ecosystems (e.g., Neo4j, AWS Neptune, TigerGraph) and graph query languages (e.g., Cypher or Gremlin).
• Experience with entity resolution / record linkage (e.g., Senzing, Quantexa, or custom probabilistic matching models).
• Ability to design flexible ontologies for evolving regulatory data (e.g., PEP and sanction list formats).
• Experience building GraphQL or REST APIs optimized for graph traversals and deep lookups.
• Strong software engineering skills in at least one of: Python, Java, Go, Rust.
• Hands-on experience building ETL/ELT data pipelines on a major cloud provider (AWS preferred).
• Experience with modern data stack tools: Spark/Flink, Kafka/Kinesis, Airflow (or managed schedulers), and warehouses (Snowflake/Redshift/BigQuery/Databricks).
• DevOps familiarity: CI/CD, Docker, Kubernetes, Terraform (infrastructure-as-code).
• Strong focus on observability (metrics/logs/traces), resilience, and early warning capabilities.

Nice-to-haves

• Experience operating a centralized data-as-a-service offering at scale (large tech or cloud-native environment).