Data Platform Engineer
Build and operate a scalable data platform centered on entity resolution, knowledge graphs, and real-time risk decisioning. The role requires strong software engineering, graph database, data pipeline, API, cloud, and observability experience.
About the job
Responsibilities
- Architect and implement entity resolution logic to deduplicate and link disparate data into unified “Golden Records” for businesses and individuals.
- Design and maintain a global business knowledge graph and ontology covering ownership chains, UBOs, and hidden risk relationships across international borders.
- Implement hybrid storage spanning graph databases, document stores, and search stores for relationship mapping, rich metadata, and adverse media.
- Optimize real-time risk assessment and multi-level ownership traversal for millisecond-scale 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 on-call operations.
- Establish best practices for data modeling, quality, lineage, governance, documentation, and trustworthy datasets.
- 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.
- Define platform architecture, service contracts, SLAs, and versioned APIs.
Requirements
- Hands-on experience with graph databases such as Neo4j, AWS Neptune, or TigerGraph and query languages such as Cypher or Gremlin.
- Proven experience with entity resolution or record linkage, using Senzing, Quantexa, or probabilistic matching models.
- Ability to design flexible ontologies for evolving regulatory data, including PEP definitions and sanctions-list formats.
- 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, Flink, Kafka, Kinesis, Airflow, Snowflake, Redshift, BigQuery, or Databricks.
- Familiarity with CI/CD, Docker, Kubernetes, and Terraform.
- Strong focus on observability, resilience, metrics, logs, traces, and early warning signals.
- Clear communication and effective cross-functional collaboration.
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 for risk detection.
Compensation & Benefits
- Medical, dental, and vision health care plans.
- Retirement plan, including 401(k) and IRA options.
- Life insurance.
- Flexible paid time off.
- Nine paid holidays.
- Family leave.
- Work-from-home support.
- Free food and snacks in Orlando.
- Wellness resources.
- Remote hires must travel to Orlando, Florida at least twice per year for town halls and team collaboration, in addition to orientation in Orlando, Florida.
Skills
Graph Databases, Cypher, Gremlin, Entity Resolution, Record Linkage, GraphQL, REST APIs, Python, Java, Go, Rust, AWS, Spark, Apache Flink, Kafka
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