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About the Role

We are seeking a Staff Data Engineer to help build and evolve the modern data platform behind Zelle, one of the largest digital payments networks in the U.S. This role is ideal for an engineer who enjoys solving large-scale distributed data challenges, influencing architecture, and building cloud-native data platforms that power next-generation Payment products, analytics, and support AI/ML workloads. You will work with modern technologies in AWS cloud while partnering with Product, Architecture, and Platform Engineering to deliver reliable, secure, and scalable data capabilities. If you're passionate about building reusable platform solutions, driving technical excellence, and making an impact at enterprise scale, we'd love to talk to you.

Responsibilities

Build data strategy for broad or complex requirements with insightful and forward-looking approaches that go beyond the direct team and solve large open-ended problems.
Participate in the strategic development of methods, techniques, and evaluation criteria for projects and programs.
Drive all aspects of technical and data architecture, design, prototyping and implementation in support of both product needs as well as overall technology data strategy.
Provide leadership and technical expertise in support of building a technical plan and backlog of stories, and then follow through on execution of design and build process through to production delivery.
Guide a broad functional area and lead efforts through the functional team members along with the team’s overall planning.
Represent engineering in cross-functional team sessions and able to present sound and thoughtful arguments to persuade others. Adapts to the situation and can draw from a range of strategies to influence people in a way that results in agreement or behavior change.
Collaborate and partner with product managers, designers, and other engineering groups to conceptualize and build new features and create product descriptions.
Actively own features or systems and define their long-term health, while also improving the health of surrounding systems.
Assist Support and Operations teams in identifying and quickly resolving production issues.
Develop and implement tests for ensuring the quality, performance, and scalability of our application.
Actively seek out ways to improve engineering and data standards, tooling, and processes.
Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data.

Requirements

Typically obtained through a Bachelor’s degree in computer science or related technical field.
Eight or more years of relevant related experience.
Seven or more years of experience in the development of complex data platform, distributed systems, SaaS, cloud solutions, micro services.
Six or more years of experience in the development of Data Warehouse, Big Data – structured & unstructured platforms, real-time & batch processing, data standards.
Four or more years of experience in development of Business Intelligent Solutions.
Two or more years of experience in development / operationalization of Artificial Intelligence / Machine Learning Models / Model development life cycle activities (implementing feature engineering, data pipelines, model operationalization, model monitoring).
Demonstrated experience in delivering business-critical systems to the market.
Ability to influence and work in a collaborative team environment.
Experience designing/developing scalable systems.
Extensive experience implementing Data Warehouse (Star / Snow flake schemas) using SQL Server or equivalent, Big Data – HDFS, Elastic Search, ETL process development using IBM Infosphere or equivalent, Reusable Frameworks.
Experience with implementing data science solutions using Python, Spark, PySpark, R, Data Robot.
Experience with event-driven architecture and messaging frameworks (Pub/Sub, Kafka, RabbitMQ, etc).
Working experience with cloud infrastructure (Google Cloud Platform, AWS, Azure, etc).
Knowledge of mature engineering practices (CI/CD, testing, secure coding, etc).
Knowledge of Software Development Lifecycle (SDLC) best practices, software development methodologies (Agile, Scrum, LEAN etc) and DevOps practices.
Attention to detail.
Background and drug screen.

Skills

Data Warehouse
Big Data
Cloud Solutions
Micro Services
Real-time Processing
Batch Processing
Data Standards
Business Intelligence
Artificial Intelligence
Machine Learning
Feature Engineering
Data Pipelines
Model Operationalization
Model Monitoring
Scalable Systems
SQL Server
HDFS
Elastic Search
IBM Infosphere
Python
Spark
PySpark
R
Data Robot
Event-driven architecture
Messaging frameworks
Pub/Sub
Kafka
RabbitMQ
Google Cloud Platform
AWS
Azure
CI/CD
Testing
Secure Coding
SDLC
Agile
Scrum
LEAN
DevOps
Tensorflow
Sage Maker
Scikit
PyCharm
Cloudera
S3
Oracle
Aerospike
Scality S3
AppDynamics
ACH/EFT
RTP
FedNow
FinTech
Kubernetes

Location

Scottsdale
San Francisco
Chicago
New York
Phoenix, AZ
Washington, DC

Work Type

Hybrid

Experience Level

Eight or more years of relevant related experience
Seven or more years of experience in the development of complex data platform, distributed systems, SaaS, cloud solutions, micro services.
Six or more years of experience in the development of Data Warehouse, Big Data – structured & unstructured platforms, real-time & batch processing, data standards.
Four or more years of experience in development of Business Intelligent Solutions.
Two or more years of experience in development / operationalization of Artificial Intelligence / Machine Learning Models / Model development life cycle activities (implementing feature engineering, data pipelines, model operationalization, model monitoring).

Education Level

Bachelor’s degree in computer science or related technical field
MS or PHD

Salary/Compensations

$175,000 - $225,000 (Phoenix, AZ/ Chicago, IL / Washington, DC)
$210,000 - $270,000 (New York, NY/ San Francisco, CA)

Benefits

Discretionary incentive plan
Healthcare Coverage – Competitive medical (PPO/HDHP), dental, and vision plans
Company contributions to your Health Savings Account (HSA) or pre-tax savings through flexible spending accounts (FSA) for commuting, health & dependent care expenses.
401(k) Retirement Plan – Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.
Paid Time Off – Flexible Time Off for Exempt (salaried) employees, as well as generous PTO for Non-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunteer day.
12 weeks of Paid Parental Leave
Maven Family Planning – provides support through your Parenting journey including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.

About the Company

At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more.
As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.

Equal Opportunity

Early Warning Services is an affirmative action and equal opportunity employer.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Early Warning Services, LLC (“Early Warning”) considers for employment, hires, retains and promotes qualified candidates on the basis of ability, potential, and valid qualifications without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote equal employment opportunity and affirmative action, in accordance with all applicable federal, state, and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our employees.