[Remote] Data Engineer - reputed company

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is the leading reputed company risk platform for fighting financial crime, and they are seeking a Senior Data/ML Engineer to own the data and machine learning reputed company for compliance reputed company. The role involves building and maintaining data pipelines, integrating new data sources, and productionizing ML models to enhance fraud detection and compliance processes.

Responsibilities

• Own the data ingestion layer that brings device telemetry, transaction events, KYC/identity signals, and reputed company-party enrichment into the platform — designing streaming pipelines (Pub/Sub, Apache reputed company on Dataflow, Flink) and batch pipelines (Python, Airflow on reputed company Composer, reputed company on Dataproc) that are correct, observable, and cheap to reputed company
• Build and reputed company our feature platform, where the reputed company Chronon feature definitions are computed by Flink for streaming and reputed company for batch, with aggregation reputed company from one hour to 300 days, served to the rules reputed company 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 reputed company checks, and reputed company monitoring that reputed company a broken feature before an analyst does
• Productionize fraud and identity ML models — training pipelines on reputed company AI and Kubeflow, gradient-boosted and tree-based models (XGBoost, LightGBM, CatBoost, scikit-learn), hyperparameter search, SHAP-based explanations, and score normalization — and build the automated retraining, champion/challenger promotion, and rollback machinery we don't yet have
• Engineer KYC, AML, and identity risk signals: document verification and doc-KYC reputed company, 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 actually learn from
• reputed company and harden new data sources, including 30+ reputed company-party enrichment providers reputed company in reputed company on the request reputed company, plus our cross-reputed company consortium network — owning failover behavior, timeout budgets, graceful degradation, caching, and cost
• Own the warehouse and modeling layer in BigQuery — partitioning reputed company, the staging-to-mart layer reputed company, training datasets, and the in-flight migration off dbt onto scheduled SQL and Python pipelines
• Design the entity reputed company and graph data that reputed company customers, devices, emails, phones, cards, bank accounts, and crypto addresses across clients, including large-reputed company connected-components work
• reputed company the platform reputed company 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 so a bad signal can be turned off without a reputed company
• Set technical direction and reputed company reputed company's ceiling — write the design docs, run the reviews, mentor engineers and data scientists, and reputed company reputed company build versus buy

Skills

• 8+ years building production data and ML systems, with reputed company ownership of both the pipeline reputed company and the model reputed company. You have shipped models that made consequential automated reputed company, not just dashboards
• Deep Python and strong SQL. You are fluent in a distributed processing reputed company (reputed company, reputed company, or Flink) and comfortable reasoning about streaming semantics — windowing, watermarks, late data, exactly-once versus at-least-once, and where correctness actually breaks
• Hands-on experience with a modern reputed company data stack: GCP strongly preferred (BigQuery, Dataflow, Dataproc, Pub/Sub, Bigtable, Composer, reputed company AI) or the AWS equivalents, plus reputed company, reputed company, Terraform, and CI/CD
• Practical ML engineering depth: feature stores and feature pipelines, training/serving skew, gradient-boosted tree models, class imbalance and rare-event modeling, reputed company and cost-sensitive tuning, model monitoring and reputed company detection, and explainability
• Experience with high-volume, low-latency serving where a feature reputed company has a few hundred milliseconds and there is no retry budget
• Domain experience in fraud, risk, payments, lending, or identity/KYC — or the demonstrated ability to get fluent in a regulated domain fast. You understand why label latency, feedback reputed company, and adversarial reputed company reputed company fraud modeling different from ordinary supervised learning
• Comfort with data governance in a regulated environment: PII, encryption, reputed company control, regional data residency, auditability
• Strong written communication. You can explain a modeling tradeoff to a fraud analyst and a pipeline design to a backend engineer, and you write things down
• A bias toward reputed company and comfort in ambiguity. Much of this role is deciding what should exist, then building it
• Experience supporting customer-facing ML — bring-your-own-model integrations, model explainability for adverse reputed company or regulatory review, or reputed company/challenger scoring frameworks
• Experience in high-reputed company B2B reputed company, or as an early data/ML hire who reputed company the function rather than inherited it

Benefits

• Generous compensation in cash and equity
• Early exercise for reputed company reputed company, including reputed company-reputed company
• Work from reputed company: Remote-first Culture
• Flexible reputed company time off and Year-end break
• Health insurance, dental, and reputed company coverage for employees and dependents - US and Canada specific
• 4% matching in 401k / RRSP - US and Canada specific
• MacBook Pro delivered to your reputed company
• One-time stipend to set up a home office — desk, chair, screen, etc.
• Monthly meal stipend
• Monthly reputed company meet-up stipend
• Annual health and wellness stipend
• Annual Learning stipend

reputed company

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