Data Platform Engineer

Responsibilities

• 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 the data platform as a true internal service with clear contracts, SLAs, and versioned APIs

Requirements

• Expertise in Graph Ecosystems: Hands-on experience with Graph databases (e.g., Neo4j, AWS Neptune, or TigerGraph) and query languages like Cypher or Gremlin
• Identity & Linkage Mastery: Proven experience with Entity Resolution or Record Linkage (e.g., using tools like Senzing, Quantexa, or custom probabilistic matching models)
• Schema Design: Ability to design flexible ontologies that handle evolving regulatory data (e.g., changing PEP definitions or Sanction list formats)
• API Performance for Graphs: Experience building GraphQL or REST APIs specifically optimized for graph traversals and deep-tree lookups
• Experience building centralized data platforms or "data-as-a-service" offerings at scale (e.g., at a large tech or cloud-native company)
• Strong software engineering skills in at least one language commonly used for data and services (e.g., Python, Java, Go, Rust)
• Hands-on experience building data pipelines and ETL/ELT workflows on a major cloud provider (AWS preferred)
• Experience with modern data stack tools such as Spark/Flink, Kafka/Kinesis, Airflow/managed schedulers, and data warehouses (e.g., Snowflake, Redshift, BigQuery, Databricks)
• Familiarity with DevOps practices: CI/CD, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform)
• Strong focus on observability (metrics, logs, traces), resilience, and building early warning signals
• Comfort collaborating cross-functionally and communicating clearly with both technical and non-technical stakeholders

Nice to Have

• Background supporting machine learning or real-time decisioning use cases from a platform point of view
• Compliance Domain Knowledge: Understanding of AML, CTF, and KYC/KYB data structures (e.g., LEIs, ISO 20022)
• Geospatial Data: Experience handling global address normalization and geospatial indexing for risk detection