Senior Data Engineer

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.

Commercial Money Movement Solution (CMS) division’s charter is to capture new sources of money movement through card and non-card flows, including Visa Business Solutions, Government Solutions and Visa Direct which presents an enormous growth opportunity. Our team brings payment solutions and associated services to clients around the globe.  Our global clients and partners deploy our solutions to serve the needs of Small Businesses, Middle Market Clients, Large Corporate Clients, Multi Nationals and Governments. 

The Visa Business Solutions (VBS) and Visa Government Solutions (VGS) team is a world-class technology organization experiencing tremendous, double-digit growth as we expand products into new payment flows and continue to grow our core card solutions. This is an incredibly exciting team to join as we expand globally. 

Essential Functions

• Work with manager and clients to fully understand business requirements and desired business outcomes
• Assist in scoping and designing analytic data assets, implementing modelled attributes and contributing to brainstorming sessions
• Build and maintain a robust data engineering process to develop and implement self-serve data and tools for Visa’s data scientists
• Perform other tasks on R&D, data governance, system infrastructure, analytics tool evaluation, and other cross team functions, on an as-needed basis
• Find opportunities to create, automate and scale repeatable analyses or build self-service tools for business users
• Execute data engineering projects ranging from small to large either individually or as part of a project team
• Ensure project delivery within timelines and budget requirements

This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager. 

Basic Qualifications:
2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience

Preferred Qualifications:
3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
Bachelor’s degree in Computer Science, Computer Engineering, Data Engineering, or a related technical field required
Master’s degree in Data Science, AI/ML, or Software Engineering preferred
3–6 years of software development experience, with a strong focus on data centric and analytics platforms
3+ years of hands on experience with Java or Scala
Core Experience:
Demonstrated expertise in modern software engineering best practices (clean code, modular design, code reviews, CI/CD)
Big Data & Distributed Systems (Modernized)
Strong hands on experience with distributed data processing frameworks, including:
Apache Spark (Core, SQL, Structured Streaming)
Hadoop ecosystem (HDFS, Hive, Hbase, MongoDB)
Apache Flink or Kafka Streams for real time processing (preferred)
Experience building highly scalable, fault tolerant, low latency data pipelines
Programming Languages:
Working proficiency in Python for data processing, ML, and automation
Familiarity with SQL and performance tuning for analytical workloads
Data Platforms & Storage
Experience with relational and NoSQL databases, including:
DB2, MySQL, PostgreSQL
NoSQL / distributed stores: HBase, Cassandra, DynamoDB, MongoDB
Experience with modern data lake and lakehouse architectures, including:
Delta Lake, Apache Iceberg, Apache Hudi
Familiarity with cloud data warehouses:
Snowflake, BigQuery, Amazon Redshift, Azure Synapse
Cloud & Infrastructure:
Hands on experience with cloud platforms:
AWS, Azure, or GCP
Experience with cloud native data services, such as:
AWS EMR, Glue, Athena
Azure Data Factory, Databricks
GCP Dataflow, Dataproc
Familiarity with containerization and orchestration:
Docker, Kubernetes (basic to intermediate)
Data Engineering & Orchestration
Experience with data pipeline orchestration tools:
Apache Airflow, Dagster, Prefect
Knowledge of data quality, lineage, and governance frameworks
Experience with schema evolution, data validation, and observability
AI / ML & GenAI (Added – Hands On)
Practical experience supporting or implementing ML pipelines, including:
Feature engineering and dataset preparation
Model training, evaluation, and batch/real time inference
Hands on exposure to ML frameworks:
scikit learn, TensorFlow, PyTorch (working knowledge)
Experience with MLOps concepts:
Model versioning, monitoring, CI/CD for ML (MLflow, SageMaker, Vertex AI)
Generative AI & Prompt Engineering (New)
Hands on experience integrating Generative AI models into data workflows:
Strong understanding of prompt engineering techniques, including:
Few shot and zero shot prompting
Prompt optimization and evaluation
Structured outputs (JSON, schemas)
Experience working with LLM APIs for:
Data enrichment
Text summarization
Semantic search and embeddings
Familiarity with RAG (Retrieval Augmented Generation) architectures and vector databases:
FAISS, Pinecone, Weaviate, OpenSearch
CI/CD, DevOps & Automation
Experience with CI/CD pipelines and automation tools:
Jenkins, GitHub Actions, GitLab CI
Experience with version control and artifact management:
Git, Artifactory, Nexus
Familiarity with infrastructure as code:
Terraform, CloudFormation (preferred)
Development Methodologies:
Experience working in Agile / Scrum environments. Strong background in Test Driven Development (TDD) and automated testing
Familiarity with data testing frameworks (Great Expectations, dbt tests)
Analytics & Data Science (Nice to Have):
Familiarity with data mining and statistical modeling techniques, including:
Regression, classification, clustering, decision trees
Ability to collaborate with data scientists and analysts to productionize models
Business & Soft Skills
Strong business acumen, able to translate business needs into scalable data solutions
Strategic thinker with a product oriented mindset
Demonstrated analytical rigor, attention to detail, and problem solving skills
Team oriented, collaborative, adaptable, and able to work across functions

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.