Data Engineer – Platform

Worth AI

. Architect and implement entity resolution logic to create unified Golden Records for businesses and individuals .

Posted 10/7/2026full-timeOrlando • Florida • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and implementing entity resolution logic, building scalable data services, and optimizing data platforms for real-time risk assessment. Proficient in developing data pipelines and APIs while ensuring platform reliability and performance.

Highest-signal resume keywords

Graph Database ExpertiseEntity Resolution ExperienceData Pipeline DevelopmentAPI Design and OptimizationCloud Computing Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Graph DatabasesEntity ResolutionData ModelingPythonJavaGoRustETL/ELT WorkflowsGraphQLREST APIs

Soft Skills

Cross-Functional CollaborationClear Communication

Tools & Technologies

AWSSparkFlinkKafkaKinesisAirflowDockerKubernetesTerraformSnowflake

Industry Keywords

AMLCTFKYCKYBGeospatial Indexing

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSBigQueryCloudDockerETLGraphQLJavaKafkaKubernetesNeo4jPythonRustSparkTerraformGo

About the role

Key responsibilities & impact

Architect and implement entity resolution logic to create unified Golden Records for businesses and individuals
Design and maintain a global business knowledge graph and ontology mapping ownership chains, UBOs, and hidden risk relationships
Implement hybrid storage across graph databases, document stores, and search stores
Optimize the platform for real-time risk assessment and millisecond multi-level ownership traversal
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 cloud-native tooling
Own platform reliability, performance, monitoring, alerting, and on-call responsibilities 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 clear contracts, SLAs, and versioned APIs

Requirements

What you’ll need

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 handling 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 at least one of 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
Ability to collaborate cross-functionally and communicate clearly 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
Nice to have: experience with global address normalization and geospatial indexing

Benefits

Comp & perks

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