Data Platform Engineer

Role Overview

As a Data Platform Engineer at Worth AI, you will design, build, and operate core data services that power product features 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.

Key Responsibilities

• Architect and implement entity resolution to deduplicate and link data into unified "Golden Records"
• Build and maintain a global business knowledge graph and ontology (including ownership chains, UBOs, and cross-border risk relationships)
• Implement a hybrid storage strategy combining graph databases (relationships) with document/search stores (metadata and adverse media content)
• Optimize for real-time risk assessment, enabling fast multi-level ownership traversal for automated Go/No-Go onboarding decisions
• Design and build scalable data services and APIs for ingesting, transforming, and serving data
• Develop batch and streaming pipelines using modern data processing frameworks on AWS
• Own reliability, performance, and API-first platform capabilities (monitoring, alerting, and on-call where appropriate)
• 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 platform architecture with clear contracts, SLAs, and versioned APIs

Requirements

• Hands-on experience with graph databases (e.g., Neo4j, AWS Neptune, TigerGraph) and graph query languages (e.g., Cypher or Gremlin)
• Proven experience with entity resolution / record linkage (e.g., Senzing, Quantexa, or custom probabilistic matching)
• Ability to design flexible ontologies/schemas that adapt to evolving regulatory data (e.g., PEP/sanctions 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 pipelines on a major cloud provider (AWS preferred)
• Experience with modern data stack tools such as Spark/Flink, Kafka/Kinesis, and Airflow/managed schedulers, plus data warehouses (e.g., Snowflake, Redshift, BigQuery, Databricks)
• Familiarity with DevOps practices: CI/CD, Docker, Kubernetes, and Terraform
• Strong focus on observability (metrics/logs/traces), resilience, and early warning/alerting

Nice-to-Haves

• Experience operating data-as-a-service / centralized platforms at scale (implied by the requirements)