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About This Role
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
Payments Ecosystem Risk \& Controls (PERC), part of the Ecosystem Risk Program (ERP), is responsible for identifying, managing, and mitigating risk across the global payments ecosystem. PERC partners with product, technology, data, architecture, operations, and regional risk teams to design and operate controls, monitoring programs, platforms, and analytics solutions that protect the integrity, security, and trust of Visa’s payment networks while enabling scalable and compliant business growth.
Position Summary
We are seeking an experienced Sr. AI Transformation and Data Engineer (Sr. Consultant level) to drive AI\-enabled solutions, enterprise data products, platform modernization, and operational excellence across ERP/PERC platforms.
This role operates at the intersection of data product management, AI, and business application support, requiring strong technical expertise, product thinking, and hands\-on execution.
The successful candidate will partner closely with business stakeholders, product managers, architects, engineers, and operations teams to design, develop, and enhance AI\-enabled scalable products, deliver innovative solutions, and operational workflows that support risk intelligence, monitoring, investigations, and compliance programs across Visa's ecosystem.
This individual contributor position requires the ability to translate complex business requirements into scalable technical and product solutions, contribute to AI transformation initiatives, support end\-to\-end delivery, and continuously improve platform capabilities through data, automation, and emerging technologies.
Key Responsibilities:
AI Transformation
- Drive AI transformation initiatives across PERC platforms by embedding Generative AI, Agentic AI, automation, copilots, and intelligent assistants into business and operational workflows.
- Define and execute AI adoption roadmaps aligned with business objectives, governance requirements, and responsible AI principles.
- Design AI\-enabled product management workflows including discovery, requirements generation, backlog refinement, UX ideation, release readiness, and operational support.
- Identify opportunities for summarization, classification, anomaly detection, investigation support, workflow automation, and decision intelligence.
- Establish success metrics and value realization frameworks for AI adoption and productivity improvements.
Product Strategy, Execution \& Delivery Ownership
- Contribute to platform strategy, roadmap planning, and product execution aligned with ERP and PERC objectives.
- Translate complex business, risk, operational, and data requirements into scalable platform capabilities.
- Lead product lifecycle activities including discovery, roadmap planning, backlog management, release planning, UAT, and launch readiness.
- Partner with architecture and engineering teams to evolve platform design, integrations, scalability, and resilience.
- Develop business cases, data\-driven recommendations, and prioritization proposals to support product and platform decisions.
Platform Support Operations
- Provide hands\-on Business Application support, including intake, triage, prioritization, and resolution of user requests and incidents.
- Manage and triage Business inquiries
- Coordinate the root cause analysis on recurring issues and drive long\-term corrective actions with technology teams.
- Support release validation, post\-release monitoring, onboarding, access management, and user enablement.
- Track and communicate operational metrics, support trends, and platform performance.
Data Products, Integration \& Governance
- Own enterprise data product strategy including ingestion, transformation, enrichment, access, and consumption requirements.
- Design scalable cloud\-based data and API contracts supporting high\-volume, multi\-source data ecosystems.
- Integrate internal Visa and external vendor data sources into unified platform capabilities.
- Define and enforce governance standards, lineage, metadata, access models, and quality controls.
- Drive data standardization and harmonization across multiple discovery sources and systems.
This position is a Hybrid position to be located in our Austin, TX office location. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- 8\+ years of relevant work experience with a Bachelor’s Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD, OR 11\+ years of relevant work experience.
Preferred Qualifications:
- 9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
- 10–12\+ years of experience in product management, analytics, AI transformation, or data platforms.
- Bachelor's degree in computer science, Engineering, Data Science, Information Systems, Business, or related field.
- 8\+ years of experience in product management, data products, platform management, analytics, or related disciplines.
- Strong experience designing and managing enterprise\-scale data products and integrations.
- Experience implementing or operationalizing Generative AI and AI\-enabled solutions.
- Hands\-on experience supporting enterprise applications, issue triage, UAT, and release management.
- Strong analytical, stakeholder management, and communication skills.
- Ability to operate effectively in highly matrixed organizations.
- Experience in enterprise AI transformation or modernization initiatives.
- Experience in payments, financial services, fintech, fraud, risk management, or compliance domains.
- Experience managing external vendors, analytics providers, and discovery data sources.
- Experience with Tableau, Power BI, or similar business intelligence platforms.
- Knowledge of AI agents, GenAI, and workflow automation platforms, and intelligent assistants.
- Experience with cloud data platforms and distributed architectures.
- Understanding of enterprise architecture and large\-scale system design.
- Demonstrated ability to influence cross\-functional stakeholders and drive execution.
U.S. Applicants Only
The estimated salary range for this position is $152,200\.00 to $ 243,700\.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job\-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.Work Hours
Varies upon the needs of the department.
Travel Requirements
This position requires travel 5\-10% of the time.
Mental/Physical Requirements
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.
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 protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
Salary Context
This $152K-$243K range is above the 75th percentile for Data Engineer roles in our dataset (median: $150K across 15 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Visa, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $178,800 based on 40 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($197K) sits 11% above the category median. Disclosed range: $152K to $243K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Visa AI Hiring
Visa has 15 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, MLOps Engineer, Data Engineer. Positions span Foster City, CA, US, Austin, TX, US, Highlands Ranch, CO, US. Compensation range: $163K - $451K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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