Sr. Data Engineer

Visa•Bellevue, WA

1d•$118,700 - $183,600•Hybrid

About The Position

The Senior Data Engineer designs, builds, and evolves large-scale data platforms and services that support reporting, analytics, and data products across Visa. The role builds scalable and reliable pipelines and reusable platform capabilities across modern cloud and big data environments, supporting batch and real-time processing at enterprise scale. The engineer develops shared platform capabilities, automation, reusable frameworks, engineering standards, and practices that improve delivery speed, observability, data quality, governance, and operational reliability. The role collaborates with engineering, analytics, product, and other technical teams to deliver end-to-end data solutions and shape Visa’s reporting and analytics platforms. It also leads technical initiatives from concept through adoption, contributes to technical reviews and best practices, and mentors engineers. The position requires at least three days in the office; the specific days are confirmed by the Hiring Manager. Work hours vary with department needs, and travel is 5–10% of the time.

Requirements

Hands-on experience designing and building enterprise-scale data platforms, pipelines, or distributed data processing solutions.
Strong experience with Spark/PySpark, Kafka, Hadoop, Hive, or comparable big data technologies.
Experience supporting both batch and real-time or streaming data processing.
Experience designing scalable data architectures and solving performance, reliability, and data quality challenges.
Experience building reusable data platforms, frameworks, shared services, or engineering capabilities used across multiple teams.
Strong understanding of data quality, governance, lineage, metadata, observability, and operational reliability.
Experience automating data engineering workflows, deployment pipelines, testing, and quality controls.
Ability to independently diagnose complex distributed-system and data-platform issues and drive them through resolution.
Ability to influence technical direction and drive initiatives across teams without direct authority.
Strong ownership and ability to move technical initiatives from design through production and broader adoption.
At least 2 years of relevant work experience and a bachelor’s degree, or at least 5 years of relevant work experience.

Nice To Haves

Experience designing and operating data platforms at enterprise scale.
Advanced experience with Apache Spark and PySpark, including performance tuning and optimization.
Experience with Kafka or comparable streaming technologies and real-time processing architectures.
Experience with orchestration technologies such as Apache Airflow.
Experience with Hadoop, Hive, distributed storage, and modern cloud data architectures.
Experience developing automated data quality, validation, monitoring, and observability frameworks.
Experience with data governance capabilities, including lineage, metadata management, and access controls.
Experience designing reusable data engineering frameworks, shared services, or self-service platform capabilities.
Experience improving CI/CD and automated delivery practices for data platforms.
Exposure to Generative AI and its application within data engineering or analytics environments.
Demonstrated ability to lead technically complex initiatives across multiple engineering teams.

Responsibilities

Design, build, and optimize large-scale data pipelines and data services for batch and real-time processing.
Develop scalable solutions using Spark, PySpark, Kafka, Hadoop, Hive, and cloud-based data platforms.
Architect data pipelines, data models, and reusable frameworks for enterprise reporting, analytics, and data products.
Build shared platform capabilities, services, and automation for use across multiple engineering teams.
Improve pipeline performance and reliability through tuning, workflow optimization, monitoring, and observability.
Automate deployment and delivery processes to improve engineering efficiency and accelerate production delivery.
Establish automated quality gates and validation practices to improve data quality, reliability, and operational stability.
Develop reusable blueprints and engineering patterns for data services and products.
Implement and strengthen data governance, including lineage, metadata management, access controls, monitoring, and validation.
Troubleshoot complex performance and reliability issues in distributed data systems and identify architectural improvements.
Partner with engineering, analytics, product, and other technical teams to design and deliver end-to-end data solutions.
Evaluate emerging technologies, including Generative AI, and identify opportunities to improve data engineering and platform capabilities.
Lead technical initiatives from concept through implementation and adoption, influencing engineering practices across teams.
Mentor engineers and contribute to technical design reviews, architecture discussions, and engineering best practices.