Key Responsibilities
Data Platform Modernization: Implement the transition from SQL Server data warehouse to the next-generation Hadoop/Databricks platform, ensuring performance, reliability, and minimal business disruption. Develop hybrid data pipelines that bridge legacy and modern ecosystems, enabling near real-time data access for analytics and AI applications. Optimize existing SQL Server models (facts, dimensions, indexes, stored procedures) and design modern equivalents in Hadoop and Spark environments. Define long-term migration strategy, data partitioning, and retention policies aligned with Visa’s data governance standards.
Data Architecture & Engineering: Implement scalable, distributed data pipelines using Spark, Kafka, Airflow, and Delta Lake. Build robust ETL/ELT frameworks to process transactional, behavioral, and unstructured data at scale. Partner with the Agentic AI team to power RAG (Retrieval-Augmented Generation) pipelines, vector database integrations and LLM data provisioning. Lead proof-of-concept (POC) initiatives to evaluate and integrate new data engineering technologies.
Operational Excellence: Provide guidance to junior Engineers to maintain existing SQL Server–based data warehouses, including database performance tuning, replication, encryption, and high-availability (HA) solutions. Implement best practices in T-SQL development, schema design, and stored procedure optimization. Perform proactive performance analysis, troubleshooting, and resolution of production issues in SQL Server and Hadoop clusters. Collaborate with DBA and application teams to ensure uptime within SLAs and compliance with Visa’s data policies.
Security, Governance & Compliance: Enforce standards for data quality, lineage, and governance across both legacy and modern platforms. Ensure full compliance with Visa’s data privacy, encryption, and access control frameworks. Partner with InfoSec to embed data security principles into every phase of data lifecycle management.
Leadership & Collaboration: Mentor and coach junior data engineers, fostering an environment of technical excellence and innovation. Collaborate cross-functionally with product, ML, and platform engineering teams on architecture decisions and roadmap execution. Serve as a bridge between legacy enterprise data teams and next-gen AI platform engineers, ensuring knowledge continuity and execution speed.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
8+ years of hands-on data engineering experience, including enterprise-scale data warehousing and pipeline design.
Proven success supporting and modernizing SQL Server–based data warehouses in high-SLA environments.
Production-level experience architecting Hadoop, Spark, and Databricks data pipelines.
Expertise in ETL/ELT frameworks, data modeling, and schema design for analytical and operational use cases.
Strong programming proficiency in Python, Java, or Scala.
Hands-on experience with AWS or Azure (Glue, Synapse, Redshift, Delta Lake, S3).
Familiarity with Kafka, Airflow, Kubernetes, and containerized data services.
Understanding of RAG pipelines, vector databases, and AI data flows.
Experience designing data pipelines and APIs compatible with Model Context Protocol (MCP)-based agent frameworks, enabling seamless integration between AI agents, data services, and enterprise APIs
Strong SQL optimization, debugging, and production troubleshooting experience.
Preferred Qualifications
Experience developing data warehouse migration or modernization from SQL Server to Hadoop/Spark ecosystems.
Deep understanding of data lineage, metadata management, and governance frameworks (e.g., Atlas, Great Expectations).
Familiarity with LangChain, LangGraph, and MCP for integrating AI agents with data systems.
Strong ability to balance innovation and stability across coexisting legacy and modern data architectures.
Proven track record mentoring engineers and collaborating across global teams.
Leadership Attributes
Go-Getter: Executes decisively and thrives in complex, hybrid data environments.
Builder: Hands-on developer who delivers scalable, production-grade data systems.
Hustler: Operates with urgency and accountability across multiple technology stacks.
Entrepreneurial: Drives innovation in data architecture and modernization.
True North: Leads with integrity and alignment to Visa’s mission and long-term data strategy.
Lead by Example: Sets high standards of excellence and transparency.
Execute with Excellence: Ensures reliability, performance, and operational maturity across all data systems.
Tech Stack Snapshot
Languages: Python, Java, Scala, SQL, T-SQL
Data Systems: SQL Server, Hadoop, Spark, Databricks, Delta Lake, Snowflake
Pipelines: Kafka, Airflow, Glue, Azure Data Factory
AI Integration: LangChain, LangGraph
Cloud Platforms: AWS, Azure
Infra: Docker, Kubernetes, Terraform, Jenkins
Governance: Great Expectations, Atlas, Data Privacy & Encryption Frameworks
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.
