Sr. Data Engineer

Visa•Bellevue, WA

4d•Hybrid

About The Position

The Senior Data Engineer will design, build, and optimize scalable data pipelines, cloud-based data solutions, and advanced analytics frameworks that support Visa's enterprise data ecosystem. This role requires deep technical expertise, cross-functional collaboration, and ownership of end‑to-end data engineering initiatives. Responsibilities: Design, build, and optimize scalable data pipelines for ingesting and processing structured and unstructured data within the Hadoop data lake and cloud environments.Architect and develop new data pipelines, data models, and analytical frameworks that provide intuitive, actionable insights to internal and external customers.Develop tools, automation, and self-service capabilities that enhance data accessibility and improve analytics efficiency across the organization.Create repeatable, automated data engineering processes that support business users and reduce manual operational efforts.Lead and execute data engineering projects of varying scope and complexity, both independently and as part of a cross-functional team.Partner with engineering, analytics, and product teams in high-visibility roles to deliver robust, end-to-end data solutions.Support the scoping, design, and development of analytic data assets and data products.Maintain and enhance data engineering best practices, governance standards, and scalable architectures across Hadoop and cloud platforms.Participate in R D initiatives, infrastructure enhancements, and cross-team technology efforts. This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager. Visa does not offer relocation assistance or immigration sponsorship for this role, now or in the future.

Requirements

2 or more years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience

Nice To Haves

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)
Proficient in Hadoop, Spark, PySpark, Scala, and Python for large‑scale data processing.
Strong SQL skills with experience in performance tuning, indexing, and query optimization.
Hands-on experience with relational and NoSQL databases, data modeling, and data warehousing.
Expertise in writing, optimizing, and tuning SQL for high performance and efficient data retrieval.
Skilled with Presto, Impala, SparkSQL, and Hive for big-data querying.
Proficient in Tableau and Power BI for analytics and visualization.
Strong experience with Databricks, including notebook/job optimization, Delta Lake, cluster management, CI/CD, and performance tuning.
Skilled in AWS, GCP, and Azure for scalable data architectures using cloud-native tools for ingestion, processing, and orchestration.
Advanced experience with Apache Spark for batch and near-real-time analytics.
Experience designing, implementing, and managing relational (RDBMS) and non-relational (NoSQL) databases.
Proficiency in data modeling and data warehousing methodologies supporting BI and analytics workloads.

Responsibilities

Design, build, and optimize scalable data pipelines for ingesting and processing structured and unstructured data within the Hadoop data lake and cloud environments.
Architect and develop new data pipelines, data models, and analytical frameworks that provide intuitive, actionable insights to internal and external customers.
Develop tools, automation, and self-service capabilities that enhance data accessibility and improve analytics efficiency across the organization.
Create repeatable, automated data engineering processes that support business users and reduce manual operational efforts.
Lead and execute data engineering projects of varying scope and complexity, both independently and as part of a cross-functional team.
Partner with engineering, analytics, and product teams in high-visibility roles to deliver robust, end-to-end data solutions.
Support the scoping, design, and development of analytic data assets and data products.
Maintain and enhance data engineering best practices, governance standards, and scalable architectures across Hadoop and cloud platforms.
Participate in R D initiatives, infrastructure enhancements, and cross-team technology efforts.