Data Platform Engineer
Build and operate a graph-centric data platform supporting entity resolution, ownership intelligence, real-time risk assessment, and data services. The role requires strong graph, data pipeline, API, cloud, and software engineering experience.
About the job
Responsibilities
- Architect and implement entity resolution to de-duplicate and link data into unified golden records.
- Design and maintain a global business knowledge graph and ontology for ownership chains, ultimate beneficial owners, and risk relationships.
- Implement hybrid storage across graph databases, document stores, and search stores.
- Optimize graph traversal for real-time risk assessment and automated onboarding decisions.
- Build scalable data services and APIs for ingesting, transforming, and serving data.
- Develop and maintain batch and streaming pipelines using modern processing frameworks and AWS tooling.
- Own platform reliability, performance, monitoring, alerting, and applicable on-call responsibilities.
- Establish data modeling, quality, lineage, governance, documentation, SLAs, and versioned APIs.
- Collaborate with data scientists, analysts, and application engineers to develop platform capabilities.
- Drive automation and standardization through CI/CD, model-as-a-service, and reproducible environments.
Requirements
- Hands-on experience with graph databases such as Neo4j, AWS Neptune, or TigerGraph.
- Experience with graph query languages such as Cypher or Gremlin.
- Proven entity resolution or record linkage experience, including probabilistic matching models or tools such as Senzing and Quantexa.
- 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.
- Experience building data pipelines and ETL/ELT workflows on a major cloud provider, preferably AWS.
- Familiarity with Spark or Flink, Kafka or 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.
- Ability to collaborate cross-functionally and communicate with technical and non-technical stakeholders.
Nice to Have
- Experience supporting machine learning or real-time decisioning use cases from a platform perspective.
- Knowledge of AML, CTF, and KYC/KYB data structures, including LEIs and ISO 20022.
- Experience with global address normalization and geospatial indexing.
Compensation and Benefits
- Medical, dental, and vision coverage.
- Retirement plan with 401(k) and IRA options.
- Life insurance.
- Flexible paid time off.
- Paid holidays.
- Family leave.
- Work-from-home support.
- Wellness resources.
- Free food and snacks in Orlando.
Skills
Graph Databases, Neo4J, Aws Neptune, Tigergraph, Cypher, Gremlin, Entity Resolution, GraphQL, REST APIs, Python, AWS, Spark, Apache Kafka, Kubernetes, Terraform
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