<div><a href="http://interface.ai/" target="_blank">interface.ai</a> is the industrys-leading specialized AI provider for banks and credit unions, serving over 100 financial institutions. The companys 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, <a href="http://interface.ai/" target="_blank">interface.ai</a> is the AI solution for banks and credit unions that want to deliver exceptional experiences and stay at the forefront of AI innovation.</div><h3>About the Role</h3> We’re hiring a Staff Engineer – Data Platform to architect, scale, and evolve the data backbone that powers all of Interface.ai’s intelligent systems. You’ll design and lead the development of a modern streaming and storage platform that unifies real-time event ingestion, distributed persistence, and secure multi-tenant access control. This is a hands-on and architecture role where you’ll build foundational systems for analytics, ML training, and personalization pipelines. You’ll define how data moves, transforms, and becomes insight — for both internal teams (data science, AI, product analytics) and external customers seeking actionable intelligence and compliance visibility. You will partner closely with the ML, AI, and Product Engineering teams to ensure the platform serves as a robust, high-throughput, and policy-compliant foundation for our next generation of AI-driven financial experiences. What You’ll Own As the Staff Engineer for Data Platform, you will own the architecture and evolution of the company’s end-to-end data ecosystem — from ingestion to analytics to model training enablement. Your scope includes: <ul> <li>Data Lake &amp; Storage Systems: Designing scalable, distributed, and cost-efficient storage architectures for multi-tenant environments.</li> <li>Streaming Infrastructure: Building and optimizing high-throughput real-time data pipelines for telemetry, transactions, and conversational data.</li> <li>Data Modeling &amp; Governance: Creating schemas, metadata systems, and access frameworks to ensure data quality, discoverability, and compliance.</li> <li>Analytics Enablement: Powering self-service analytics, KPI dashboards, and behavioral insights for internal stakeholders and customers.</li> <li>ML Enablement: Providing clean, secure, and well-modeled data for training custom ML models that drive personalization, upsell, and cross-sell.</li> </ul> What You’ll Do <ul> <li>Architect &amp; Build Data Lake: Design and deploy a multi-tenant, secure data lake that supports structured, semi-structured, and unstructured data across internal and customer domains.</li> <li>Develop Streaming Systems: Build and scale streaming platforms (Kafka, Flink, Spark Streaming, Pulsar, etc.) for ingesting billions of events per day in near real-time.</li> <li>Implement Distributed Storage: Lead efforts to design or extend distributed storage systems (e.g., Iceberg, Delta Lake, Parquet-based stores) with strong RBAC and data versioning.</li> <li>Enable Real-Time Analytics: Build internal APIs and data services for fast, ad-hoc querying and BI tools. Data Governance &amp; Compliance: Define and implement role-based access control, encryption at rest and in transit, and audit logging for regulatory alignment (SOC2, GDPR).</li> <li>Collaborate with ML/AI Teams: Build feature stores, training datasets, and automated pipelines for model training, personalization, and predictive analytics. </li> <li>Optimize Cost &amp; Performance: Continuously refine storage and compute efficiency, partition strategies, and caching layers to ensure cost-effective scalability.</li> <li>Mentor and Lead: Guide senior data and backend engineers, establish engineering standards for data reliability, schema evolution, and observability.</li> </ul> What We’re Looking For Required Qualifications <ul> <li>8+ years of experience in Data Infrastructure, having built and scaled data infrastructure, distributed systems, or data platforms.</li> <li>Hands-on expertise and deep understanding of event-driven architecture with prior experience managing large Streaming Systems Mastery and working with tools such as: Kafka, Flink, Spark Streaming, or Pulsar. </li> <li>Proven experience designing or implementing Distributed Storage Systems including architecting data lakehouse using Iceberg, Delta Lake, Hudi, or custom solutions</li> <li>Expertise in architecting secure, multi-tenant &amp; RBAC data systems with strong isolation, access control, and data governance policies. </li> <li>Strong understanding of Data Modeling; hands-on experience with schema design, dimensional modeling, and data warehouse/lakehouse best practices. </li> <li>Proficiency in Python, Scala, or Java, with experience in building high-performance ETL/ELT frameworks.</li> <li>Deep expertise in Cloud &amp; Infrastructure  with AWS (S3, Glue, Athena, Redshift, EMR, Kinesis) or GCP/Azure equivalents.  </li> </ul> Preferred Experience <ul> <li>Experience implementing data mesh or domain-oriented data architecture.</li> <li>Knowledge of feature store design for ML use cases.</li> <li>Familiarity with dbt, Airflow, or similar orchestration tools.</li> <li>Prior experience working in FinTech, banking, or high-compliance environments. </li> </ul> What Makes This Role Special <ul> <li>You’ll define the core data architecture that powers intelligence, analytics, and personalization across millions of users.</li> <li>You’ll work across boundaries — from data streaming and storage to analytics enablement and ML training pipelines.</li> </ul> You’ll lead with autonomy: balancing deep technical design, performance optimization, and business impact. Compensation <ul> <li> Compensation is expected to be between $210,000 - $270,000. Exact compensation may vary based on skills and location.</li> </ul> What We Offer <ul> <li>💡 100% paid health, dental &amp; vision care</li> <li>💰 401(k) match &amp; financial wellness perks</li> <li>🌴 Discretionary PTO + paid parental leave</li> <li>🏡 Remote-first flexibility</li> <li>🧠 Mental health, wellness &amp; family benefits</li> <li>🚀 A mission-driven team shaping the future of banking</li> </ul>  <div>At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not  discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each persons unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.</div>