Data Engineer

Sardine is the leading agentic risk platform for fighting financial crime, unifying data across risk teams to stop fraud in real time and automate fraud and AML operations. We are looking for a Senior Data/ML Engineer to own the data and machine learning foundation that Sardine's compliance decisions run on. This role owns pipelines end to end: how data arrives, how it becomes a feature, how that feature becomes a model, and how that model stays correct in production. You will write production code, make architectural calls, and work directly with data scientists, backend engineers, and fraud analysts.

• Own the data ingestion layer: streaming pipelines (Pub/Sub, Apache Beam on Dataflow, Flink) and batch pipelines (Python, Airflow on Cloud Composer, Spark on Dataproc).
• Build and evolve the feature platform using Chronon feature definitions computed by Flink for streaming and Spark for batch.
• Establish feature correctness: streaming-versus-batch reconciliation, recomputation tests, train/serve parity checks, drift monitoring.
• Productionize fraud and identity ML models: training pipelines on Vertex AI and Kubeflow, gradient-boosted models (XGBoost, LightGBM, CatBoost, scikit-learn), hyperparameter search, SHAP explanations, score normalization, automated retraining, champion/challenger promotion.
• Engineer KYC, AML, and identity risk signals: document verification, sanctions/PEP/adverse-media screening, email and phone risk, synthetic identity indicators, bank and account verification, periodic customer due diligence.
• Integrate and harden new data sources: 30+ third-party enrichment providers, cross-client consortium network, failover, timeout budgets, caching, cost.
• Own the warehouse and modeling layer in BigQuery: partitioning, staging-to-mart, training datasets, migration off dbt onto scheduled SQL and Python pipelines.
• Design entity resolution and graph data linking customers, devices, emails, phones, cards, bank accounts, crypto addresses.
• Make the platform safe by construction: field-level encryption, regional data residency, PII handling, feature-level gating.
• Set technical direction: write design docs, run reviews, mentor engineers and data scientists.

• 8+ years building production data and ML systems, with ownership of both pipeline and model sides.
• Deep Python and strong SQL.
• Fluent in a distributed processing framework (Spark, Beam, or Flink) and streaming semantics.
• Hands-on experience with a modern cloud data stack: GCP strongly preferred (BigQuery, Dataflow, Dataproc, Pub/Sub, Bigtable, Composer, Vertex AI) or AWS equivalents, plus Docker, Kubernetes, Terraform, CI/CD.
• Practical ML engineering depth: feature stores, training/serving skew, gradient-boosted tree models, class imbalance, threshold tuning, model monitoring, explainability.
• Experience with high-volume, low-latency serving.
• Domain experience in fraud, risk, payments, lending, or identity/KYC.
• Comfort with data governance in a regulated environment: PII, encryption, access control, regional data residency, auditability.
• Strong written communication and a bias toward action.

• Experience supporting customer-facing ML: bring-your-own-model integrations, model explainability for adverse action, shadow/challenger scoring.
• Experience in high-growth B2B SaaS or as an early data/ML hire.

• Generous compensation in cash and equity
• Early exercise for all options, including pre-vested
• Remote-first culture
• Flexible paid time off and year-end break
• Health insurance, dental, and vision coverage for employees and dependents (US and Canada)
• 4% matching in 401k / RRSP (US and Canada)
• MacBook Pro delivered
• One-time home office setup stipend
• Monthly meal stipend
• Monthly social meet-up stipend
• Annual health and wellness stipend
• Annual learning stipend