Sr. Manager; Data Engineering

About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.

At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.

Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.

Job Description

Role Summary:

Visa's Data Trust & Platform Engineering (DTPE) organization delivers the foundational platforms that power Visa's enterprise data, analytics, AI, and cloud ecosystem. DTPE is responsible for advancing secure, scalable, governed, and resilient data platform capabilities spanning hybrid-cloud infrastructure, compute, storage, streaming, orchestration, data governance, privacy, quality, and platform engineering services.

We are seeking an experienced Senior Manager, Platform Engineering to lead a team of highly skilled engineers responsible for building, operating, and modernizing critical platform services that power Visa's data, analytics, and AI ecosystem. This leader will drive engineering execution, platform reliability, cloud transformation, automation, and customer success while fostering a culture of technical excellence and innovation.

The ideal candidate combines strong technical depth, people leadership, operational excellence, and a passion for building scalable platforms that serve enterprise-wide customers.

All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.

Key Responsibilities:

Technical & Platform Leadership:

- Lead the design, development, deployment, and operation of large-scale platform engineering solutions supporting analytics, AI, data processing, and enterprise workloads.
- Drive architecture and implementation of highly scalable, reliable, secure, and cost-efficient platform services.
- Partner closely with architects, principal engineers, and product stakeholders to define technical roadmaps and engineering priorities.
- Lead platform modernization initiatives across hybrid-cloud environments, cloud-native architectures, container platforms, and developer self-service capabilities.
- Establish engineering standards and best practices covering scalability, resiliency, observability, automation, security, and performance.

Cloud & Data Platform Engineering:

- Lead adoption of cloud-native technologies across AWS, Azure, Kubernetes, Databricks and modern platform engineering ecosystems.
- Drive development of reusable platform services for data engineering, analytics, AI/ML workloads, and enterprise applications.
- Enable large-scale distributed compute and storage platforms supporting petabyte-scale data processing.
- Build and operationalize data platform capabilities including orchestration, streaming, metadata management, governance, and workload automation.
- Improve platform efficiency, utilization, and cost optimization through automation and infrastructure engineering.

Engineering Delivery & Operational Excellence:

- Own engineering execution, service delivery, platform availability, and customer commitments.
- Drive continuous improvements in reliability, scalability, incident reduction, and operational maturity.
- Lead production readiness reviews, incident management, root-cause analysis, and remediation efforts.
- Define and track operational KPIs including service availability, latency, reliability, customer satisfaction, and engineering productivity.
- Champion Site Reliability Engineering (SRE) principles, observability, alerting, and capacity planning practices.

Generative AI & Intelligent Engineering:

- Drive adoption of GenAI-powered engineering capabilities including AI-assisted development, code generation, intelligent troubleshooting, and operational automation.
- Partner with AI and platform teams to enable secure and governed GenAI infrastructure and services.
- Identify opportunities to leverage LLMs, retrieval-augmented generation (RAG), AI agents, and automation frameworks to improve engineering productivity and customer experience.
- Promote responsible AI practices, governance controls, security, and compliance requirements for enterprise AI solutions.

People Leadership:

- Lead, mentor, and develop high-performing engineering teams across multiple locations.
- Drive hiring, talent development, performance management, and career growth for engineers and technical leads.
- Foster a culture focused on technical excellence, innovation, accountability, inclusion, and continuous learning.
- Encourage knowledge sharing, technical mentorship, and engineering craftsmanship across teams:

Customer & Cross-Functional Collaboration:

- Partner with Data Engineering, Product, Infrastructure, Security, Operations, and Architecture teams to deliver scalable platform capabilities.
- Act as an advocate for platform customers by improving platform usability, self-service adoption, and developer experience.
- Collaborate with business and engineering stakeholders to prioritize investments and deliver measurable business outcomes.
- Communicate engineering progress, risks, operational health, and roadmap updates to leadership.

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.

Qualifications

Basic Qualifications:

- 10+ years of software engineering, platform engineering, cloud engineering, infrastructure engineering, or data platform engineering experience.
- 3+ years of experience leading engineering teams and delivering large-scale enterprise platforms.
- Strong hands-on experience designing and operating distributed systems in production environments.
- Experience with cloud platforms such as AWS and/or Azure.
- Experience with container orchestration technologies such as Kubernetes and cloud-native architectures.
- Strong knowledge of software engineering fundamentals, system design, networking, storage, security, and performance optimization.
- Experience building highly available, fault-tolerant, and scalable distributed services.
- Experience driving automation using Infrastructure as Code (Terraform, CloudFormation, etc.) and CI/CD practices.
- Demonstrated experience leveraging GenAI, machine learning, or intelligent automation technologies to improve engineering outcomes.

Preferred Qualifications:

Cloud & Infrastructure:

- Strong knowledge of AWS services such as EKS, EC2, S3, EMR, Glue, Lambda, ECS, and IAM.
- Strong knowledge of Databricks services
- Experience with Azure services including AKS, Data Factory, Synapse, Fabric, Storage, and Entra ID.
- Experience building hybrid-cloud platform solutions spanning on-premises and cloud environments.
- Expertise in Kubernetes, container management, service mesh, and cloud-native infrastructure.

Data Platform Engineering:

- Experience operating enterprise-scale data platforms.
- Knowledge of Hadoop, Spark, Trino/Presto, Kafka, Databricks, Snowflake, Delta Lake, Iceberg, or similar technologies.
- Experience supporting large-scale batch, streaming, AI, and analytics workloads.
- Understanding of metadata, governance, data quality, lineage, privacy, and access management capabilities.

Platform Engineering & SRE

- Experience with developer platforms and self-service engineering models.
- Strong understanding of observability platforms such as Prometheus, Grafana, Datadog, Splunk, ELK, or OpenTelemetry.
- Experience implementing SRE practices including SLOs, SLIs, error budgets, reliability engineering, and operational automation.
- Strong troubleshooting and performance tuning experience across distributed environments.

Generative AI:

- Understanding of LLM architectures and enterprise GenAI platforms.
- Experience with Retrieval-Augmented Generation (RAG), vector databases, embeddings, AI agents, and prompt engineering.
- Familiarity with GenAI frameworks such as LangChain, LangGraph, Semantic Kernel, Crew AI, Autogeny, or equivalent.
- Understanding of responsible AI, model governance, observability, security, and privacy requirements.

Leadership Expectations:

- Technical Leadership & Engineering Excellence
- Customer-Centric Platform Mindset
- Execution with Excellence
- Data-Driven Decision Making
- Platform Reliability & Operational Excellence
- Innovation Through Automation and AI
- Talent Development and Coaching
- Collaboration Across Teams
- Ownership and Accountability
- Continuous Learning and Growth

Visa is an EEO Employer

Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.