V

Visa

Runs a global network for digital payments

Senior Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeBellevue, WA, USA

About the Job

Requirements

  • At least 2 years of relevant work experience and a bachelor's degree, or at least 5 years of relevant work experience.
  • 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.

Responsibilities

  • Design, build, and optimize large-scale data pipelines and data services supporting 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 supporting enterprise reporting, analytics, and data products.
  • Build shared platform capabilities, services, and automation for adoption across multiple engineering teams.
  • Improve data pipeline performance and reliability through performance tuning, workflow optimization, monitoring, and observability.
  • Automate deployment and delivery processes to improve engineering efficiency and reduce the time required to bring new data capabilities to production.
  • Establish automated quality gates and validation practices to improve data quality, reliability, and operational stability.
  • Develop reusable blueprints and engineering patterns for new data services and data products.
  • Implement and strengthen data governance practices, including data lineage, metadata management, access controls, monitoring, and validation.
  • Troubleshoot complex performance and reliability issues across distributed data systems and identify opportunities for architectural improvement.
  • 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.

Desired Qualifications

  • 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.

About the Company

Visa

Runs a global network for digital payments

Visa runs a global payment network that connects banks, merchants and consumers so card purchases and money transfers move between financial institutions. Its VisaNet system handles authorization, clearing and settlement, linking the bank behind a buyer's card with the bank that accepts a merchant's payment. The company earns fees from payment activity, transaction processing and international transactions, and sells fraud prevention tools, payment software and consulting services to businesses. Banks and other partners issue Visa branded cards and use its technology to build their own payment products. Visa traces its roots to a Bank of America card program launched in 1958 and is headquartered in San Francisco.

Company size
10,001+
Company stage
N/A
Total funding
$24.2M
Headquarters
San Francisco, CA
Founded
1958

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