Data Platform Engineer
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Job Description
Data Platform Engineer building data pipelines, APIs, and risk knowledge graphs for Worth AI’s AI-powered decision-making products. Supporting real-time onboarding and trusted data services.
Responsibilities:
- Architect and implement entity resolution logic to de-duplicate and link disparate data into unified Golden Records
- Design and maintain a global business knowledge graph and ontology for ownership chains, UBOs, and hidden risk relationships
- Implement hybrid storage combining graph databases with document and search stores
- Optimize real-time risk assessment and multi-level ownership traversal for automated onboarding decisions
- Design and build scalable data services and APIs for ingesting, transforming, and serving data
- Develop and maintain batch and streaming data pipelines using modern frameworks and AWS cloud-native tooling
- Own platform reliability, performance, monitoring, alerting, and on-call activities where appropriate
- Implement data modeling, quality, lineage, and governance best practices
- Collaborate with data scientists, analysts, and application engineers to translate needs into platform capabilities
- Drive automation and standardization through CI/CD, model as a service, and reproducible environments
- Define and evolve the data platform architecture with 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
- Proven experience with Entity Resolution or Record Linkage (e.g., Senzing, Quantexa, or custom probabilistic matching models)
- Ability to design flexible ontologies for evolving regulatory data
- Experience building GraphQL or REST APIs optimized for graph traversals and deep-tree lookups
- Experience building centralized data platforms or data-as-a-service offerings at scale
- Strong software engineering skills in Python, Java, Go, or Rust
- Hands-on experience building data pipelines and ETL/ELT workflows on a major cloud provider; AWS preferred
- Experience with Spark/Flink, Kafka/Kinesis, Airflow or managed schedulers, and data warehouses such as Snowflake, Redshift, BigQuery, or Databricks
- Familiarity with CI/CD, Docker, Kubernetes, and Terraform
- Strong focus on observability, resilience, and early warning signals
- Comfort collaborating cross-functionally and communicating with technical and non-technical stakeholders
- Nice to have: Background supporting machine learning or real-time decisioning use cases
- Nice to have: Understanding of AML, CTF, and KYC/KYB data structures, including LEIs and ISO 20022
- Nice to have: Experience with global address normalization and geospatial indexing
Benefits:
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance
- Flexible Paid Time Off
- 9 paid Holidays
- Family Leave
- Work From Home
- Free Food & Snacks (Orlando)
- Wellness Resources
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