Descripción de la oferta
About Us Staq is a leading Banking-as-a-Service (BaaS) and embedded finance platform, transforming the way businesses integrate banking and financial services. At Staq, we empower our clients to innovate, expand, and streamline their financial services offerings using our cutting-edge platform. Our mission is to bridge the gap between traditional banking and the digital era by providing seamless, scalable, and secure financial solutions.
Our agents, recommendation systems, and automations are only as good as the data they consume. A recommendation engine needs well-structured feature data. Right now that data plumbing doesn't have a dedicated owner. As we scale from one product to an SDK that multiple banking applications use, the data layer becomes a shared dependency that every AI feature builds on top of. This role owns the pipelines that feed the intelligence platform, the evaluation data that tells us if our AI is working, and the infrastructure that lets us iterate on data quality without slowing down AI development.
Build and maintain the data pipelines that transform raw financial data (Plaid transactions, bank accounts, credit data, subscription records) into the enriched context that agents consume at runtime
Ensure data freshness, quality, and consistency across all pipelines feeding the intelligence platform
Evaluation & Observability Data
Build the data infrastructure for AI evaluation — collecting agent decisions, recommendation results, automation outcomes, and user feedback into queryable, analyzable datasets
Own the LLM observability data layer — structured collection of call latencies, token usage, cost per flow, error rates, and model performance metrics across all agent and automation flows
Create dashboards and data products that let the AI team measure agent quality, recommendation relevance, automation success rates, and LLM operational health
Support A/B testing and experiment tracking data infrastructure so we can iterate on AI behavior with evidence, not intuition
SDK Data Contracts
Design data contracts and schemas that serve both Zeen and future banking applications that plug into the intelligence platform SDK
Own the ingestion layer for partner and third-party data sources — as the SDK expands to other banks, each will bring their own data formats and integration patterns
Data Quality & Operations
Own data quality monitoring, validation, and alerting across all pipelines
Build data lineage tracking so we can trace any agent decision back to the data that informed it
Ensure PII handling in data pipelines aligns with platform policy — financial data requires careful treatment, and the AI layer has strict boundaries around what data reaches LLMs
Python for pipeline development; SQL for analytics and data modeling
Financial data sources: Plaid, partner APIs, internal domain services (banking, credit, subscriptions, journal/ledger)
OpenTelemetry traces and structured artifacts as data sources for AI evaluation
Cloud-native infrastructure; Financial data with strict handling requirements
3+ years building and operating production data pipelines
~ Strong Python and SQL; experience with data transformation frameworks
~ Experience designing schemas and data contracts for consumption by application services or ML/AI systems
~ Understanding of data quality practices — validation, monitoring, alerting on pipeline failures
~ Comfort working with sensitive financial data and understanding why data handling discipline matters
Experience building data infrastructure that feeds AI/ML systems (feature stores, context pipelines, evaluation datasets)
Familiarity with observability data (OpenTelemetry, structured logs) as a data source
Experience building monitoring and analytics for LLM systems — latency tracking, cost attribution, and performance dashboards
Experience with data lineage, audit trails, or data governance
Experience designing data contracts for multi-tenant or multi-product platforms
