Data Platform Engineer

Role Overview

Data Platform Engineer at Worth AI. You will design, build, and operate core data services that power product analytics and internal/external data consumers. Treat the data platform like a product with strong SLAs, reliable self-service, and clear API contracts.

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 covering ownership chains, UBOs, and hidden risk relationships.
• Build a hybrid storage strategy combining graph databases 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.
• Develop scalable data services and APIs for ingesting, transforming, and serving high-quality data.
• Build and maintain batch + streaming pipelines using modern processing frameworks and AWS cloud tooling.
• Own reliability and performance (monitoring, alerting, and on-call where appropriate) and enforce API-first platform standards.
• Apply best practices for data modeling, quality, lineage, and governance with well-documented datasets.
• Partner with data scientists, analysts, and application engineers to translate requirements into robust platform capabilities.
• Drive automation/standardization via CI/CD, model-as-a-service, and reproducible environments.
• Evolve 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 (Cypher or Gremlin).
• Strong entity resolution / record linkage experience (e.g., Senzing, Quantexa, or custom probabilistic matching).
• Ability to design flexible ontologies for evolving regulatory formats (e.g., PEP/sanctions definition changes).
• Experience building graph-optimized APIs, including GraphQL or REST for deep-tree traversals.
• Strong software engineering skills in at least one data/service language (e.g., Python, Java, Go, Rust).
• Hands-on ETL/ELT and data pipelines on a major cloud provider (AWS preferred).
• Familiarity with modern data stack tools: Spark/Flink, Kafka/Kinesis, Airflow (or managed schedulers), and warehouses (Snowflake/Redshift/BigQuery/Databricks).
• DevOps practices: CI/CD, Docker, Kubernetes, Terraform (IaC).
• Strong observability and reliability mindset (metrics, logs, traces, resilience, and early-warning signals).