Data Platform Engineer

As a Data Platform Engineer, you will design, build, and operate the core data services that power our products and analytics. You’ll own end-to-end data pipelines and API services that ingest, process, and expose high-quality data to internal customers (data science, analytics, product, and other engineering teams) and external partners.

What You Will Do

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, implement a hybrid storage strategy, optimize the platform for real-time risk assessment, and design and build scalable data services and APIs.

Why It Might Be a Fit

You will be part of a small, high-impact team that treats the data platform as a product with strong SLAs, and reliable self-service for internal and external users. You will have the opportunity to work closely with data scientists, analysts, and application engineers to understand their needs and translate them into robust platform capabilities.

Requirements

• 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., using tools like Senzing, Quantexa, or custom probabilistic matching models)
• Ability to design flexible ontologies that handle evolving regulatory data (e.g., changing PEP definitions or Sanction list formats)
• 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

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