Job Description
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Company Overview
A leading global financial technology organization is building a unified payments and financial platform that empowers businesses worldwide with integrated solutions across business accounts, payments, spend management, treasury, and embedded finance. With a global presence and a strong focus on innovation, the company is investing heavily in data, artificial intelligence, and machine learning to shape the future of financial services.
The organization’s Knowledge Platform team sits at the center of its data and AI strategy, building the foundational infrastructure that enables teams across the business to leverage data, AI, and machine learning to drive meaningful business outcomes. The team develops and manages platforms that support the entire data and AI/ML lifecycle while ensuring scalability, governance, and security.
As a Staff Software Engineer, Data Platform Engineering, the successful candidate will oversee the strategy, architecture, development, and operations of the company’s data and AI platforms. This role requires a highly experienced technical leader who can influence data-driven decision-making, mentor engineering teams, and drive innovation in a fast-paced environment.
Key Responsibilities
- Lead the identification and resolution of company-wide challenges through advanced data platform solutions.
- Provide technical vision and leadership across the organization’s data ecosystem while actively contributing to complex problem-solving initiatives.
- Establish and advocate best practices across the data platform to promote engineering excellence, innovation, and craftsmanship.
- Mentor and support the professional and technical development of engineers and peers.
- Define and influence technical roadmaps across multiple teams and stakeholders.
- Drive the architecture, development, and operation of large-scale data and AI platforms.
- Support the evolution of AI agent-ready infrastructure to enable analytics, natural language querying, and real-time decision-making capabilities.
- Collaborate with cross-functional teams to deliver scalable and impactful business solutions.
- Evaluate emerging technologies and lead proof-of-concept initiatives to inform future architectural decisions.
Required Qualifications
- Minimum of 8 years of experience in Data Platform Engineering or an equivalent combination of professional and academic experience in a quantitative field.
- Proven track record of leading large-scale initiatives across multiple teams and influencing technical roadmap planning.
- Experience collaborating with diverse stakeholders to deliver measurable business outcomes.
- Demonstrated ability to balance execution speed with research, statistical understanding, and scalable system design.
- Strong mentoring experience, supporting the growth and development of engineers, scientists, and peers.
- Experience providing technical leadership across projects involving ETL frameworks, metrics stores, infrastructure management, and data security.
- Proven expertise in building, deploying, and maintaining reliable, geographically distributed data pipelines at scale.
- Familiarity with workflow and orchestration frameworks such as Airflow, DBT, or similar technologies.
Preferred Qualifications
- Hands-on experience designing data processing patterns for modern Lakehouse architectures.
- Experience developing standard framework modules, high-performance services, and client libraries for big data environments.
- Proficiency with technologies such as GCP, Databricks, BigQuery, DataProc, Kafka, Kubernetes, Spark, DataFlow, Google Cloud Storage, and Airflow.
- Excellent written and verbal communication skills, with the ability to communicate effectively across technical and executive audiences.
- Strong ability to assess new technologies and execute proof-of-concept initiatives to support architectural decisions.
- Experience succeeding in highly complex and rapidly evolving environments.
Disclaimer
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