Education and/or experience typically obtained through a Bachelor's degree in Computer Science or a related technical field.
Twelve or more years of relevant professional experience.
Nine or more years of experience designing and developing complex data-intensive systems, including data platforms, distributed systems, SaaS, and cloud-based solutions.
Two or more years of experience building end-to-end data management platforms, including data modeling, data governance, BI/reporting, and ML lifecycle support.
Five or more years of experience with ETL or data pipeline development (e.g., Ab Initio, Talend, Informatica, or comparable tools) and BI/reporting solutions (e.g., Tableau, Business Objects, Grafana, or similar tools).
Experience working with advanced analytics or ML workloads using tools such as Python, PySpark, or Spark.
Hands-on experience with Docker and containerized workloads.
Experience designing and developing scalable, highly available systems.
Experience with event-driven architectures and messaging frameworks (e.g., Pub/Sub, Kafka, RabbitMQ).
Hands-on experience with cloud infrastructure platforms (GCP, AWS, Azure, or equivalent).
Strong knowledge of mature engineering practices, including CI/CD, automated testing, secure coding, SDLC best practices, Agile methodologies, and DevOps practices.
Proven ability to influence and collaborate across multiple teams and departments.
Background and drug screen.
Master's or PhD in Computer Science or a related field.
Understanding of ML frameworks or platforms such as TensorFlow, SageMaker, or Scikit-learn.
Experience with big data platforms and object storage (e.g., Cloudera, S3).
Experience with relational and enterprise database platforms (e.g., Oracle, SQL Server).
Strong programming experience in Python and PySpark; familiarity with R is a plus.
Knowledge of Aerospike, Scality S3, or Elasticsearch.
Experience with monitoring and alerting systems (e.g., AppDynamics).
Knowledge of ACH/EFT and real-time payment networks (RTP, FedNow).
FinTech domain experience.
Kubernetes experien
Benefits
Health insurance
Additional Information
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.
Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment.
Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.
Overall Purpose
This position is a senior technical leadership role responsible for the development, testing, deployment, and long-term health of complex, large-scale data engineering solutions. The role drives data platform and architecture decisions across teams and contributes to the company's overall technology and data strategy.
Essential Functions
Partner with software engineering, product, and architecture teams to shape data engineering approaches and share knowledge across the organization.
Own the data and technical strategy for broad or complex requirements, taking forward-looking approaches that extend beyond a single team and address large, open-ended problems.
Define and influence department-wide data architecture, design patterns, and code standards.
Review and validate the effectiveness, quality, and scalability of code produced by multiple teams.
Be accountable for resolving technical conflicts within and across teams.
Drive technical architecture, design, prototyping, and implementation in support of product needs and overall technology and data strategy.
Represent engineering in cross-functional forums; present clear, well-reasoned technical arguments and influence alignment and outcomes.
Collaborate with product managers, designers, and engineering groups to conceptualize and build new data-driven features.
Actively own data platforms, pipelines, or systems and define their long-term health, while improving the reliability and scalability of surrounding systems.
Assist Support and Operations teams in identifying and resolving production issues.
Develop and implement tests and validation mechanisms to ensure data quality, performance, and scalability.
Mentor and develop other engineers and serve as a technical leader on cross-functional initiatives.
Proactively identify opportunities to improve engineering standards, tooling, and processes.
Support the company's commitment to risk management and the protection of the integrity, availability, and confidentiality of systems and data.
Early Warning (zelle) · Chicago