Interface AI
Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant.
Interface.ai
is the agentic AI layer for modern banking—safe, accountable AI agents that resolve issues, personalize experiences, and drive outcomes across all interaction channels (voice, chat, employee‑assist). We partner with 100+ U.S. banks & credit unions, delivering secure, auditable automation with tangible performance gains. More than 100 financial institutions rely on our banking‑specific AI and domain expertise to move fast, stay secure, and measure impact. We help financial institutions deliver better customer experiences and improved operating performance at scale.
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.
About the Role 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:
Data Lake & Storage Systems:
Designing scalable, distributed, and cost‑efficient storage architectures for multi‑tenant environments.
Streaming Infrastructure:
Building and optimizing high‑throughput real‑time data pipelines for telemetry, transactions, and conversational data.
Data Modeling & Governance:
Creating schemas, metadata systems, and access frameworks to ensure data quality, discoverability, and compliance.
Analytics Enablement:
Powering self‑service analytics, KPI dashboards, and behavioral insights for internal stakeholders and customers.
ML Enablement:
Providing clean, secure, and well‑modeled data for training custom ML models that drive personalization, upsell, and cross‑sell.
What You’ll Do
Architect & 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.
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.
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.
Enable Real‑Time Analytics:
Build internal APIs and data services for fast, ad‑hoc querying and BI tools.
Data Governance & Compliance:
Define and implement role‑based access control, encryption at rest and in transit, and audit logging for regulatory alignment (SOC2, GDPR).
Collaborate with ML/AI Teams:
Build feature stores, training datasets, and automated pipelines for
model training, personalization, and predictive analytics.
Optimize Cost & Performance:
Continuously refine storage and compute efficiency, partition strategies, and caching layers to ensure cost‑effective scalability.
Mentor and Lead:
Guide senior data and backend engineers, establish engineering standards for data reliability, schema evolution, and observability.
What We’re Looking For Required Qualifications
8+ years of experience in
Data Infrastructure , having built and scaled data infrastructure, distributed systems, or data platforms.
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.
Proven experience designing or implementing
Distributed Storage Systems
including architecting data lakehouse using
Iceberg ,
Delta Lake ,
Hudi , or custom solutions.
Expertise in architecting
secure, multi‑tenant & RBAC data systems
with strong isolation, access control, and data governance policies.
Strong understanding of
Data Modeling ; hands‑on experience with
schema design ,
dimensional modeling , and
data warehouse/lakehouse best practices .
Proficiency in
Python ,
Scala , or
Java , with experience in building high‑performance ETL/ELT frameworks.
Deep expertise in
Cloud & Infrastructure
with
AWS (S3, Glue, Athena, Redshift, EMR, Kinesis)
or
GCP/Azure
equivalents.
Preferred Experience
Experience implementing
data mesh
or
domain‑oriented data architecture .
Knowledge of
feature store design
for ML use cases.
Familiarity with
dbt ,
Airflow , or similar orchestration tools.
Prior experience working in
FinTech ,
banking , or
high‑compliance environments .
What Makes This Role Special
You’ll define the
core data architecture
that powers intelligence, analytics, and personalization across millions of users.
You’ll work across boundaries — from
data streaming and storage
to
analytics enablement and ML training pipelines.
You’ll lead with autonomy: balancing deep technical design, performance optimization, and business impact.
Compensation
Compensation is expected to be between $210,000 - $270,000. Exact compensation may vary based on skills and location.
What We Offer
401(k) match & financial wellness perks
Discretionary PTO + paid parental leave
Mental health, wellness & family benefits
A mission‑driven team shaping the future of banking
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 person’s 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.
#J-18808-Ljbffr
Interface.ai
is the agentic AI layer for modern banking—safe, accountable AI agents that resolve issues, personalize experiences, and drive outcomes across all interaction channels (voice, chat, employee‑assist). We partner with 100+ U.S. banks & credit unions, delivering secure, auditable automation with tangible performance gains. More than 100 financial institutions rely on our banking‑specific AI and domain expertise to move fast, stay secure, and measure impact. We help financial institutions deliver better customer experiences and improved operating performance at scale.
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.
About the Role 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:
Data Lake & Storage Systems:
Designing scalable, distributed, and cost‑efficient storage architectures for multi‑tenant environments.
Streaming Infrastructure:
Building and optimizing high‑throughput real‑time data pipelines for telemetry, transactions, and conversational data.
Data Modeling & Governance:
Creating schemas, metadata systems, and access frameworks to ensure data quality, discoverability, and compliance.
Analytics Enablement:
Powering self‑service analytics, KPI dashboards, and behavioral insights for internal stakeholders and customers.
ML Enablement:
Providing clean, secure, and well‑modeled data for training custom ML models that drive personalization, upsell, and cross‑sell.
What You’ll Do
Architect & 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.
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.
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.
Enable Real‑Time Analytics:
Build internal APIs and data services for fast, ad‑hoc querying and BI tools.
Data Governance & Compliance:
Define and implement role‑based access control, encryption at rest and in transit, and audit logging for regulatory alignment (SOC2, GDPR).
Collaborate with ML/AI Teams:
Build feature stores, training datasets, and automated pipelines for
model training, personalization, and predictive analytics.
Optimize Cost & Performance:
Continuously refine storage and compute efficiency, partition strategies, and caching layers to ensure cost‑effective scalability.
Mentor and Lead:
Guide senior data and backend engineers, establish engineering standards for data reliability, schema evolution, and observability.
What We’re Looking For Required Qualifications
8+ years of experience in
Data Infrastructure , having built and scaled data infrastructure, distributed systems, or data platforms.
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.
Proven experience designing or implementing
Distributed Storage Systems
including architecting data lakehouse using
Iceberg ,
Delta Lake ,
Hudi , or custom solutions.
Expertise in architecting
secure, multi‑tenant & RBAC data systems
with strong isolation, access control, and data governance policies.
Strong understanding of
Data Modeling ; hands‑on experience with
schema design ,
dimensional modeling , and
data warehouse/lakehouse best practices .
Proficiency in
Python ,
Scala , or
Java , with experience in building high‑performance ETL/ELT frameworks.
Deep expertise in
Cloud & Infrastructure
with
AWS (S3, Glue, Athena, Redshift, EMR, Kinesis)
or
GCP/Azure
equivalents.
Preferred Experience
Experience implementing
data mesh
or
domain‑oriented data architecture .
Knowledge of
feature store design
for ML use cases.
Familiarity with
dbt ,
Airflow , or similar orchestration tools.
Prior experience working in
FinTech ,
banking , or
high‑compliance environments .
What Makes This Role Special
You’ll define the
core data architecture
that powers intelligence, analytics, and personalization across millions of users.
You’ll work across boundaries — from
data streaming and storage
to
analytics enablement and ML training pipelines.
You’ll lead with autonomy: balancing deep technical design, performance optimization, and business impact.
Compensation
Compensation is expected to be between $210,000 - $270,000. Exact compensation may vary based on skills and location.
What We Offer
401(k) match & financial wellness perks
Discretionary PTO + paid parental leave
Mental health, wellness & family benefits
A mission‑driven team shaping the future of banking
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 person’s 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.
#J-18808-Ljbffr