Data Engineer - Sr. Consultant level

Visa•Bellevue, WA

3d•Hybrid

About The Position

The Data Engineer - Sr Consultant is a senior technical contributor responsible for designing, optimizing, and scaling key components of Visa's 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. Sr Consultant Engineers act as domain experts, partnering across teams to elevate data architecture, improve reliability, and guide the technical growth of the broader engineering organization. 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. This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager.

Requirements

8 or more years of relevant work experience with a Bachelor Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD
Strong expertise in distributed data processing using Hadoop, Apache Spark, and modern data lake/lakehouse architectures.
Advanced programming proficiency in PySpark, Scala, and Python, with experience building production-grade data applications.
Deep knowledge of SQL, distributed query engines (Presto, Trino, Hive, SparkSQL), and database performance optimization.
Hands-on experience designing and managing relational databases, NoSQL systems, and scalable storage solutions.
Proficiency in data modeling, ETL/ELT patterns, and data warehousing design.
Strong experience with AWS, GCP, and Azure for cloud-based data architectures, including data lakes, orchestration tools, and distributed compute.
Advanced proficiency with Databricks, including: Notebook and job optimization Delta Lake design and tuning Cluster and workspace management CI/CD integration for data workloads Performance tuning for distributed processing
Ability to lead technical efforts across teams, troubleshoot complex issues, and deliver scalable, resilient data solutions.
Strong communication and collaboration skills to work effectively with product, analytics, and engineering partners.

Nice To Haves

9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD

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.