Senior Staff Full-stack Engineer, Global Payments Network
$314.8k - $359.3kCapital One Bank
Capital One has grown from a promising credit card startup into a Fortune 100 company. Our businesses span Card, Retail Banking, Commercial Banking, and Financial Services, backed by an Enterprise Services group that is Capital One's digital and technology backbone. Within that enterprise, the Global Payment Network (GPN) is one of the company's most ambitious builds: the unification of three legacy networks — Discover Network, PULSE, and Diners Club International — into a single, modern payment network. Rather than three separate acceptance footprints, three separate data models, and three separate operating teams, GPN is being rebuilt from a clean sheet so that a card issued anywhere on the network works everywhere on the network. This is the kind of ground-up platform build most engineers only read about — a chance to define how data moves through one of the largest payment networks in the world, not maintain what's already there. What You'll Do: The Senior Staff Full-stack Engineer (IC) for GPN will be responsible for architecting and delivering the real-time and batch data infrastructure that every other team on the network builds on — ingestion, streaming, the canonical data model, and the catalog and metadata layer that gives the network a single source of truth. This role sits at the cross-section of Core Network, Fraud/AML, Analytics, and the Modeling teams building on top of this data, and offers the rare opportunity to define how a payment network's data platform gets built from the ground up rather than inheriting one. A deep understanding of distributed systems, real-time streaming architecture, data modeling at scale, and cloud infrastructure is required. This is a highly visible and influential individual-contributor role, with no direct reports, that requires a combination of deep technical skill, setting vision and architecture, and effective partnership across engineering, data science, product, and risk. Key Responsibilities:
- Architect the real-time, event-driven data pipelines that move authorization, transaction, settlement, and fraud-relevant events across the network with the latency and exactly-once delivery guarantees a payment network requires — this is not a dashboard-refresh pipeline, it is infrastructure fraud and risk decisioning will run on.
- Define the canonical data model that reconciles three legacy schemes — each with its own merchant hierarchies, participant identities, and transaction semantics — into one shared model, with legacy formats translated at the network's edge only, never propagated inward.
- Design and build the catalog and metadata layer (schema registry, lineage, data contracts, access policy) that gives every downstream team — fraud models, analytics, canonical modeling — one trustworthy source of truth instead of each team reverse-engineering its own.
- Set the technical architecture and standards for streaming, storage, and low-latency serving at network scale, and serve as an evangelist for data standardization both internally and with the partner teams building on this platform.
- Operate as the deep technical authority and trusted advisor across the data engineering function, reimagining what a modern, portable data platform should look like — favoring open, well-understood components over one-off tooling, so the platform is something new engineers can reason about on day one.
- Partner directly with Fraud/AML, Analytics, and Canonical Modeling leadership so your architecture directly unblocks their roadmaps rather than trailing behind them.
- Mentor and raise the technical bar for engineers across the organization, acting as a force multiplier on a brand-new network build without owning a reporting chain of your own.
- Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 9 years of experience in all of the following:
- Software engineering and solution architecture
- Enterprise architecture and design patterns
- Cloud computing (AWS, Microsoft Azure, Google Cloud)
- Data architecture
- Master’s Degree in Computer Science or Software Engineering
- 12+ years of experience in all of the following:
- Software engineering and solution architecture
- Enterprise architecture and design patterns
- Web technologies (JavaScript, TypeScript and SPA frameworks)
- Designing and building distributed HPC and ML systems
- Cloud computing (AWS, Microsoft Azure, Google Cloud)
- Data architecture
- Deep experience designing event-driven, real-time streaming architectures at scale (e.g. Kafka, Flink, or equivalent)
- Experience with distributed storage, catalog, and metadata/lineage systems (e.g. Iceberg, data lakehouse architectures)
- Experience designing canonical or shared data models that unify disparate or legacy systems
- Experience architecting high-availability, low-latency distributed systems in a regulated, high-stakes domain (payments, fraud, financial transaction processing, or similar)
$286.2k - $326.7k
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