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Data Engineer - Onboarding

Sardine· Engineering
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Remote$150k - $205k + equityFull-timePosted 2mo ago

About the role

Senior Data/ML Engineer to own Sardine's data and machine learning foundation for onboarding, including data ingestion, feature platform, ML model training/production, and integration with fraud/risk signals.

Responsibilities

  • Own the data ingestion layer that brings device telemetry, transaction events, KYC/identity signals, and third-party enrichment into the platform — designing streaming pipelines (Pub/Sub, Apache Beam on Dataflow, Flink) and batch pipelines (Python, Airflow on Cloud Composer, Spark on Dataproc) that are correct, observable, and cheap to extend.
  • Build and evolve our feature platform, where Chronon feature definitions are computed by Flink for streaming and Spark for batch, with aggregation windows from one hour to 300 days, served to the rules engine and to models under a sub-second budget.
  • - Establish feature correctness as an engineering discipline: streaming-versus-batch reconciliation, recomputation tests against the warehouse, train/serve parity checks, and drift monitoring that catches a broken feature before an analyst does.
  • - Productionize fraud and identity ML models — training pipelines on Vertex AI and Kubeflow, gradient-boosted and tree-based models (XGBoost, LightGBM, CatBoost, scikit-learn), hyperparameter search, SHAP-based explanations, and score normalization — and build automated retraining, champion/challenger promotion, and rollback machinery.
  • - Engineer KYC, AML, and identity risk signals: document verification and doc-KYC outcomes, sanctions/PEP/adverse-media screening results, email and phone risk, synthetic identity indicators, bank and account verification, and periodic customer due diligence — turning noisy, multi-vendor, multi-jurisdiction data into features a model can learn from.
  • - Integrate and harden new data sources, including 30+ third-party enrichment providers called in parallel on the request path, plus cross-client consortium network — owning failover behavior, timeout budgets, graceful degradation, caching, and cost.
  • - Own the warehouse and modeling layer in BigQuery — partitioning strategy, staging-to-mart layer cake, training datasets, and in-flight migration off dbt onto scheduled SQL and Python pipelines.
  • - Design the entity resolution and graph data linking customers, devices, emails, phones, cards, bank accounts, and crypto addresses across clients, including large-scale connected-components work.
  • - Make the platform safe by construction: field-level encryption for sensitive identifiers, regional data residency enforced in the pipeline definitions, PII handling and deletion paths, and feature-level gating to turn off bad signals without a deploy.
  • - Set technical direction and raise the team's ceiling — write design docs, run reviews, mentor engineers and data scientists, and decide what to build vs. buy.

Qualifications

  • 8+ years building production data and ML systems, with ownership of both pipeline and model sides.
  • Deep Python and strong SQL. Fluent in distributed processing frameworks (Spark, Beam, or Flink) and proficient with streaming semantics (windowing, watermarks, late data, exactly-once vs at-least-once).
  • Hands-on experience with a modern cloud data stack: GCP (BigQuery, Dataflow, Dataproc, Pub/Sub, Bigtable, Composer, Vertex AI) or AWS equivalents, plus Docker, Kubernetes, Terraform, and CI/CD.
  • Practical ML engineering depth: feature stores/pipelines, training/serving skew, gradient-boosted tree models, class imbalance/rare-event modeling, threshold and cost-sensitive tuning, model monitoring and drift detection, explainability.
  • Experience with high-volume, low-latency serving where a feature fetch has a few hundred milliseconds and limited retry budget.
  • Domain experience in fraud, risk, payments, lending, or identity/KYC, or ability to quickly learn a regulated domain.
  • Comfort with data governance in regulated environments: PII, encryption, access control, regional residency, auditability.
  • Strong written communication; ability to explain modeling tradeoffs to fraud analysts and pipeline designs to backend engineers.

Skills

Required:PythonSQLDistributed data processingCloud data stack

Benefits

  • Generous compensation in cash and equity
  • Early exercise for all options, including pre-vested
  • Work from anywhere: Remote-first Culture
  • Flexible paid time off and Year-end break
  • Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific
  • 4% matching in 401k / RRSP - US and Canada specific
  • MacBook Pro delivered to your door
  • One-time stipend to set up a home office — desk, chair, screen, etc.
  • Monthly meal stipend
  • Monthly social meet-up stipend
  • Annual health and wellness stipend
  • Annual Learning stipend
Last updated Oct 1, 2026. Interested? Apply on the company site.

About Sardine

The AI risk platform for fraud, credit, and compliance

Sardine’s AI platform is at the core of enterprise risk and fraud workflows, allowing them to consolidate vendors and improve operational efficiency. Hundreds of enterprises in over 70 countries trust Sardine to stop fraud in real-time, streamline BSA/AML compliance, and unify data across risk teams.

fraud preventionKYC/AMLTransaction monitoringBot detectionScams and ATOCard issuing fraudAML MonitoringCustomer dashboard
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