Data Engineer - Onboarding

Sardine
North America, United States, Canada
Workplace: RemoteFull timeUSD 150,000 - 205,000 annuallyFunction: Data Science & Machine LearningExperience: 8+ yearsSkills: ["Extreme ownership","Strong written communication","Mentor","Bias toward action","Comfort in ambiguity"]

Own end-to-end data and machine learning foundations for compliance decisions, including onboarding, fraud, KYC/AML, and identity risk. Build and operate streaming and batch ingestion pipelines, evolve a feature platform (Chronon + Flink/Spark), establish feature correctness and drift monitoring, and productionize fraud/identity models using GCP ML services. Work closely with data scientists, backend engineers, and fraud analysts to set technical direction and ship reliable fraud ML.

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FursaFursa
Sardine
Sardine
1 month ago

Data Engineer - Onboarding

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Source: Company careers pageValidated by: Fursa AI
Last checked: 10 hours agoStatus: Live

Job Summary

Own end-to-end data and machine learning foundations for compliance decisions, including onboarding, fraud, KYC/AML, and identity risk. Build and operate streaming and batch ingestion pipelines, evolve a feature platform (Chronon + Flink/Spark), establish feature correctness and drift monitoring, and productionize fraud/identity models using GCP ML services. Work closely with data scientists, backend engineers, and fraud analysts to set technical direction and ship reliable fraud ML.
Location: North America, United States, Canada
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Own data ingestion pipelines bringing device telemetry, transaction events, KYC/identity signals, and third-party enrichment, using streaming and batch architectures that are correct and observable.
  • •Build and evolve a feature platform using Chronon definitions computed by Flink (streaming) and Spark (batch), supporting wide aggregation windows and low-latency serving.
  • •Establish feature correctness with reconciliation, recomputation tests, train/serve parity checks, and drift monitoring to prevent broken features from reaching analysts.
  • •Productionize fraud and identity ML models, including training pipelines on Vertex AI and Kubeflow, model explainability (SHAP), and automated retraining/champion-challenger/rollback workflows.
  • •Engineer KYC/AML and identity risk signals and harden new data sources, owning the warehouse/modeling layer in BigQuery and the entity resolution/graph data linking cross-client entities.

Pay and Benefits

Salary: USD 150,000 - 205,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVision401kRrspRemote WorkPaid LeaveHome OfficeMeal StipendLearning Budget

Key Requirements

  • •8+ years building production data and ML systems with end-to-end ownership of pipelines and models, shipped models enabling automated decisions.
  • •Deep Python and strong SQL, with fluency in distributed processing (Spark, Beam, or Flink) and streaming semantics (windowing, watermarks, exactly-once vs at-least-once).
  • •Hands-on experience with a modern cloud data stack, strongly GCP (BigQuery, Dataflow, Dataproc, Pub/Sub, Bigtable, Composer, Vertex AI) or AWS equivalents, plus Docker, Kubernetes, Terraform, and CI/CD.
  • •Practical ML engineering depth including feature stores/pipelines, training/serving skew, tree models, rare-event modeling, threshold tuning, monitoring, drift detection, and explainability.
  • •Domain and governance comfort for regulated environments, including fraud/risk/payments/identity-KYC plus PII, encryption, access control, and auditability.
Experience:8+ yearsFraudRiskPaymentsLendingIdentityKYCML systemsFraud ML
Skills:Extreme ownershipStrong written communicationMentorBias toward actionComfort in ambiguity
Tech Stack:Pub/SubApache BeamDataflowFlinkPythonAirflowCloud ComposerSparkDataprocChrononRules engineVertex AIKubeflowXGBoostLightGBMCatBoostScikit-learnSHAPBigQueryDbt

Company Brief

Sardine
Provides fraud prevention and account security solutions using machine learning to detect and block account takeover, payment fraud, and malicious actors for digital platforms and marketplaces.
Industry: Cybersecurity
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