Director, Data Engineering

Visa•Bellevue, WA

1d•$197,200 - $315,700•Hybrid

About The Position

The Director, Data Engineering leads the design, development, and deployment of scalable, secure data pipelines, platforms, and ML/AI infrastructure using cloud-native technologies and big data frameworks. The role oversees the integration of structured and unstructured data for advanced analytics, machine learning, and AI initiatives, and evaluates and champions tools and technologies that improve data engineering productivity, scalability, and effectiveness. The Director establishes data engineering best practices, technical standards, governance, and quality standards. They ensure security, privacy, compliance, performance, scalability, and availability across data engineering projects and platforms; define and report KPIs; and lead incident response and root cause analysis to support platform reliability. The role collaborates across functions and with senior leadership to deliver data-driven solutions and execute enterprise data strategy, while mentoring and growing a high-performing engineering team. Visa expects employees in this role to work in the office at least three days per week, with exact days confirmed by the Hiring Manager. Hours vary according to department needs, and travel is required 5–10% of the time. The position is performed in an office setting.

Requirements

At least 10 years of relevant work experience with a bachelor’s degree, or 13 or more years of relevant work experience.
Experience leading large-scale data engineering teams and projects in a complex enterprise environment.
Experience with data architecture, ETL/ELT pipelines, data warehousing, and real-time data processing.
Experience with big data technologies such as Spark, Kafka, or Hadoop.
Experience with data modeling, governance, and quality frameworks.
Experience delivering high-impact data solutions that drive business outcomes.
Experience designing and building ML infrastructure to train or serve models.
Hands-on development experience building real-time and streaming infrastructure, with DevOps excellence.
Advanced architectural experience with large-scale event-driven systems using technologies such as Apache Kafka, Apache Pulsar, AWS Kinesis, or Google Cloud Pub/Sub.
Hands-on mastery or architectural oversight of real-time processing frameworks such as Apache Flink, Apache Spark Streaming, Kafka Streams, or Ray.
Deep knowledge of low-latency storage systems, column-oriented databases, and OLAP query engines such as Apache Pinot, Apache Iceberg, ClickHouse, Cassandra, or Redis.
Experience building multi-tenant streaming platforms with isolation, rate limiting, dynamic resource allocation, and zero-downtime schema evolution using formats such as Protobuf or Avro.
Demonstrated success implementing SLOs, SLAs, error budgets, and distributed tracing and monitoring systems such as Prometheus, Grafana, Datadog, or OpenTelemetry.
Excellent problem-solving and analytics skills, with the ability to form and advocate an independent viewpoint.
Experience in financial services, payments, or another highly regulated industry.
Experience mentoring and developing talent within engineering teams.
Digital fluency, including the ability to use emerging technologies such as generative AI tools to support everyday work.

Nice To Haves

12 or more years of work experience with a bachelor’s degree, or 8–10 years with an advanced degree, or 6 or more years with a PhD.
A master’s or PhD in Computer Science or a related technical discipline.
Experience working on large open-source projects, preferably ClickHouse, Redis, Flink, Kafka, or Ray.
Contributions to open-source data engineering tools or communities.
Experience driving operational excellence and standard methodologies in an engineering environment.

Responsibilities

Lead the design, development, and deployment of scalable and secure data pipelines, platforms, and ML/AI infrastructure.
Oversee integration of structured and unstructured data sources to support advanced analytics, machine learning, and AI initiatives.
Evaluate, implement, and champion tools and technologies to improve data engineering productivity, scalability, and effectiveness.
Establish and enforce data engineering best practices, technical standards, governance, and quality standards.
Ensure data security, privacy, compliance, performance, scalability, and availability across data engineering projects and platforms.
Define, monitor, and report KPIs measuring the success and impact of data engineering activities.
Lead incident response, root cause analysis, and resolution for data platform issues to ensure availability and reliability.
Collaborate with cross-functional teams to deliver data-driven business solutions and define technical roadmaps.
Mentor, coach, and develop data engineers, fostering innovation, excellence, and continuous learning.
Partner with senior leadership to define, execute, and communicate enterprise data strategy.
Promote a GenAI-first culture by helping engineers integrate generative AI into daily workflows.