Quantexa Engineer

Location: NYC or Charlotte, NC(Uptown) Hybrid: Onsite 3x per week Interview process: Resume Review 2/27 at 10 AM- Slot candidates into interview Monday-Wednesday the following week. Quantexa Engineer (GCP | Elasticsearch | Scala/Java) About the Role We're seeking a Quantexa Engineer to build and scale data-driven decision intelligence solutions leveraging the Quantexa platform. You'll design and implement entity resolution, network/graph analytics, and risk detection use cases across large-scale GCP data environments. The ideal candidate has hands-on engineering experience with Scala or Java, Elasticsearch, and modern big data pipelines-and can translate business requirements into production-grade Quantexa solutions. What You'll Do Design & Build Quantexa Solutions Implement entity resolution (ER), matching rules, network generation, and contextual analytics using Quantexa SDKs and configuration frameworks. Develop custom Quantexa scoring, models, and rules for AML, fraud, KYC/CDD, credit risk, customer 360, or supply chain risk. Data Engineering on GCP Ingest, transform, and model large-scale datasets using BigQuery, Dataflow (Apache Beam), Dataproc (Spark), Pub/Sub, and Cloud Storage. Build CI/CD-enabled data pipelines (e.g., Cloud Build, Terraform, GitHub Actions) and optimize for performance and cost. Search & Retrieval Design and tune Elasticsearch indices for entity and network search; implement analyzers, relevance tuning, aggregations, and resilient query patterns for high QPS workloads. Software Engineering Develop microservices or batch jobs in Scala or Java, integrating Quantexa APIs/SDKs. Implement unit/integration tests and quality gates; ensure observability (logging, metrics, tracing). Data Quality & Governance Establish data profiling, standardization, and deduplication practices; define match/merge thresholds and survivorship strategies. Partner with InfoSec to meet security, privacy, and compliance requirements (e.g., SOC 2, ISO 27001; AML/KYC in FS contexts). Production Operations Deploy and operate Quantexa components on GCP (Kubernetes/GKE or managed services). Monitor performance, troubleshoot pipelines, and drive continuous improvement. Required Qualifications 3-7+ years of software or data engineering experience (open to leveling up or down). Hands-on Quantexa implementation experience (projects involving ER, scoring rules, and network/graph building). Strong programming in Scala or Java (collections, concurrency, functional patterns preferred). Solid GCP big data expertise: BigQuery, Dataflow/Beam, Dataproc/Spark, Pub/Sub, Cloud Storage, Cloud Composer (or Airflow). Elasticsearch: schema design, indexing strategies, analyzers/tokenizers, query DSL, performance tuning, cluster ops basics. Experience with Spark (RDD/DataFrame APIs), SQL, and data modeling for analytics/graph workloads. CI/CD and DevOps fundamentals: Git, Cloud Build/GitHub Actions, Terraform (or similar), Containerization (Docker), GKE. Strong understanding of data quality, matching logic, and survivorship rules. Excellent communication skills and the ability to partner with business/SME teams. Nice to Have Financial Services use cases: AML, sanctions screening, fraud, KYC/CDD, correspondent banking, trade surveillance. Graph technologies: Neo4j, GraphFrames, Gremlin, NetworkX. Streaming: Kafka / Pub/Sub → Dataflow pipelines for near-real-time ER. Observability: Cloud Monitoring, Cloud Logging, Prometheus/Grafana. Security & governance: IAM, VPC-SC, KMS, DLP tooling. Python for data tooling and orchestration. Familiarity with Quantexa Workbenches, visualization, and case management integrations. Education / Certifications (Preferred) Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field. Google Cloud Professional Data Engineer or Professional Cloud Architect. Elastic Certified Engineer (plus). Quantexa training/certifications (nice to have; employer-funded if not present).

Created: 2026-03-04