Staff Backend Engineer - Data

interface.ai is the industry's-leading specialized AI provider for banks and credit unions, serving over 100 financial institutions. The company's integrated AI platform offers a unified banking experience through voice, chat, and employee-assisting solutions, enhanced by cutting-edge proprietary Generative AI.

Our mission is clear: to transform the banking experience so every consumer enjoys hyper-personalized, secure, and seamless interactions, while improving operational efficiencies and driving revenue growth.

interface.ai offers pre-trained, domain-specific AI solutions that are easy to integrate, scale, and manage, both in-branch and online. Combining this with deep industry expertise, interface.ai is the AI solution for banks and credit unions that want to deliver exceptional experiences and stay at the forefront of AI innovation.

About the Role

We are looking for a Staff Backend Engineer with deep experience in data-intensive systems, modern data pipelines, and backend services that operate at scale.

You will design and build critical components of our Data Platform, enabling structured ingestion, transformation, real-time and batch processing, and integration with AI workflows. This role combines system design, backend engineering, and platform thinking—with the opportunity to influence core architectural direction.

You’ll work closely with product managers, data scientists, infra and application engineers to ensure our data platform is reliable, scalable, and AI-ready.

Key Responsibilities

• Architect Multi-Tenant Data Infrastructure - Build data systems with strong tenant isolation guarantees, while supporting tenant-specific configuration, schema extensions, and custom data ingestion pipelines.
  
  
• Enable Probabilistic and Attribution-Driven Models - Lay the backend foundations for models that operate on probabilistic reasoning, sequence attribution, and temporal causality. Partner with data scientists to expose necessary event logs, intermediate representations, and counterfactual support for explainable outcomes.
  
  
• Ensure data integrity, lineage, and auditability across tenants with dynamic configuration needs.
  
  
• Design, build, and maintain scalable backend services that support data ingestion, data pipelines, metadata management, and the data warehouse
  
  
• Work across streaming and batch paradigms, building frameworks that support high-velocity real-time events as well as durable historical datasets
  
  
• Collaborate on the architecture of our next-generation data platform—balancing flexibility, performance, and cost
  
  
• Implement observability, SLAs, and fault tolerance across critical data flows
  
  
• Partner with ML and analytics teams to enable data products such as behavioral modeling, conversation intelligence, and user analytics
  
  
• Build and maintain APIs, microservices, and orchestration systems that expose data pipelines as first-class infrastructure

• Drive technical excellence through design reviews, code quality, documentation, and mentorship
  
  

What Success Looks Like

• Scalable ingestion systems that support structured, semi-structured, and event-based data
  
  
• A unified data layer powering internal analytics and external-facing dashboards
  
  
• Backend systems that are robust, monitored, and support low-latency query and job execution
  
  
• Data infrastructure that is modular, reusable, and developer-friendly across use cases
  
  
• Increased data reliability, accuracy, and freshness across mission-critical services

What You Bring

Required

• 7+ years of backend engineering experience with a focus on data-intensive applications
  
  
• Experience designing backend systems in multi-tenant, configuration-rich environments.
  
  
• A high attention to detail and craftsmanship around data correctness, versioning, and recovery.
  
  
• Strong command of backend development using languages such as Python, Go, or Java
  
  
• Experience with building distributed systems, streaming (Kafka, Pulsar, etc.), or batch orchestration (Airflow, Dagster, dbt)
  
  
• Strong knowledge of modern data architecture principles, including data lakes, lakehouses, or warehouse-first designs
  
  
• Experience working with cloud-native data warehouses (e.g., Snowflake, BigQuery, Redshift) and storage systems (e.g., S3, GCS)
  
  
• Understanding of schema management, versioning, metadata systems, and data governance
  
  
• Familiarity with performance tuning, concurrency, and reliability in backend services
  
  
• Comfort working with stochastic and causal reasoning frameworks, and the system-level implications for such modeling (e.g., replay, feature provenance, sequence entropy).

Why Join Us

• Work on critical infrastructure at the heart of AI-first products in financial services
  
  
• Solve real-world challenges in scale, latency, observability, and developer ergonomics
  
  
• Collaborate with an exceptional team of engineers, data scientists, and product leaders
  
  
• Shape the architecture of a modern platform powering AI agents and real-time intelligence