Staff Data Engineer – Visa

Opportunity Overview

Technical Architecture & Lifecycle Workflow

The Staff Data Engineer takes end-to-end ownership of enterprise data products, translating functional business requirements into secure, high-throughput batch and real-time data pipelines:

┌───────────────────────────────────────────┐
│ 1. Architecture & Design                 │ ➔ Requirement translation, component modeling, governance standards
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. Pipeline Implementation & Orchestration│ ➔ Batch/Real-time streaming (Kafka, Hadoop, Hive, SQL/NoSQL)
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. Automated Testing & MLOps/GenAI        │ ➔ Unit/e2e testing, security scans, GenAI/ML service integrations
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. Deployment, CI/CD & Observability      │ ➔ Docker/K8s, Jenkins/Artifactory, production telemetry, patching
└───────────────────────────────────────────┘

Key Responsibilities

• End-to-End Pipeline Ownership: Design, build, and optimize batch and real-time data extraction, transformation, and ingestion workflows.

• Architecture & Reusable Code: Translate complex business requirements into modular architecture patterns; enforce coding standards through hands-on code reviews.

• CI/CD & Automation Infrastructure: Build standard automation processes for data deployment, pipeline orchestration, and server security remediations using Docker, Kubernetes, and Jenkins.

• Observability & Troubleshooting: Monitor live data processes, analyze operational metrics, and author diagnostic runbooks to resolve pipeline bottlenecks.

• Data Governance & AI Integration: Enforce data retention, privacy, and quality standards across data products while leveraging Generative AI tools (e.g., Copilot) and cloud ML services.

Technical & Qualification Requirements

Baseline Qualifications

• Education & Experience:

  • Bachelor’s degree + 6 to 9 years of relevant data engineering experience OR

  • Master’s/Advanced degree + 5+ years of relevant experience (or Ph.D. with up to 3 years).

• Core Technical Competencies: Hands-on experience in distributed pipeline engineering, automated testing, containerization/orchestration, CI/CD, and secure coding practices.

Preferred Technical Skill Matrix

Application & Assessment Preparation

1. Distributed System Design: Practice designing low-latency, fault-tolerant data pipelines that process high-concurrency payment streams.

2. Database Querying & Optimization: Review partition strategies, indexing, and query tuning in relational and NoSQL databases.

3. DevOps & Pipeline Automation: Be prepared to discuss container deployment with Kubernetes, CI/CD pipeline configuration, and production error-handling strategies.