Staff Data Engineer

Visa•Bellevue, WA

3h•Hybrid

About The Position

The Staff Data Engineer is a senior technical contributor responsible for designing, optimizing, and scaling key components of Acceptance data platforms across Hadoop and cloud environments. This role delivers high-impact data solutions, drives advanced engineering best practices, and leads technical design for complex initiatives. Staff Engineers act as domain experts, partnering across teams to elevate data architecture, improve reliability, and guide the technical growth of the broader engineering organization.

Requirements

5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.

Nice To Haves

Strong expertise with Hadoop and Apache Spark for large-scale distributed data processing.
Advanced programming skills in PySpark, Scala, and Python for building production-grade data pipelines.
Deep experience with SQL, distributed query engines (Presto, Trino, Hive, SparkSQL), and database/query performance tuning.
Hands-on experience with both relational (RDBMS) and NoSQL data systems.
Proficiency in data modeling, ETL/ELT design, and data warehousing methodologies.
Skilled in architecting cloud-based data solutions on AWS, GCP, and Azure.
Advanced proficiency with Databricks, including Delta Lake, job optimization, cluster management, CI/CD, and performance tuning.
Ability to lead technical initiatives, mentor engineers, and deliver scalable, reliable data solutions.
Strong communication and collaboration skills to work effectively with product, analytics, and engineering partners.

Responsibilities

Architect and implement large-scale data pipelines, ingestion frameworks, and processing systems that support analytics, real-time insights, and enterprise workloads.
Lead the design and development of scalable data models, lakehouse structures, and distributed compute solutions across Hadoop and cloud platforms.
Provide deep technical guidance during design reviews, code reviews, and architectural discussions, influencing engineering decisions within assigned domains.
Build automation frameworks, reusable components, and self-service tooling that improve platform efficiency and reduce operational overhead.
Drive platform improvements in data quality, observability, governance, reliability, and performance.
Lead multi-team technical projects, collaborating closely with engineering, analytics, and product partners to deliver high-impact data solutions.
Evaluate emerging tools and technologies, recommending enhancements that strengthen scalability and data engineering productivity.
Mentor Senior and Mid-level Engineers, fostering technical growth and reinforcing engineering best practices.
Support strategic initiatives such as cloud migrations, pipeline optimization, data lake/lakehouse modernization, and platform tuning.