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Staff Data Engineer - job 13 of 50

The primary purpose of this role is to serve as a pivotal technical link between the Data Engineering team and the Product organization. Reporting directly to the Data Engineering department, this position will be responsible for executing and aligning data engineering strategies and processes in accordance with Technology Services standards. This person will work closely with the Data Solutions Product Manager to identify and undertake key projects and responsibilities that drive product development and innovation. As a bridge between technical and product realms, this role will ensure seamless integration of data engineering solutions into product initiatives, fostering a data-driven product development culture, and contributing to the overall growth and success of the organization.

 

Essential Functions:

  • Acts as a key player during meetings with stakeholders (e.g., Product teams, business partners, Cybersecurity) to identify and clarify requirements and determine business needs.
  • Identifies systems, architectures, tools, and storage needs that support the requirements for the application for a product.
  • Proficient in analyzing data for the purpose of intentional database design, discovering new potential data use cases and articulating technical requirements.
  • Implements extensible, maintainable and reusable code, using appropriate coding patterns, guidelines, styles and best practices, informally mentoring others, and adheres to all security requirements.
  • Independently conducts unit testing to confirm functional capability of code. Conducts tests for coding standards and security scans. Conducts user acceptance testing in collaboration with customer.
  • Builds tools and standard automation processes that assist in transforming, managing, accessing, deploying, and monitoring data processes in batches and in real time.
  • Independently writes queries to extract and compile raw data across end-to-end pipelines.
  • Implements and monitors self-healing processes across multiple product features to prevent recurring issues, maintain and improve data quality and pipeline, and optimize performance throughout the data lifecycle.

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

Average salary estimate

$110000 / YEARLY (est.)
min
max
$90000K
$130000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Staff Data Engineer, Visa

At Mentor, we are on the lookout for a passionate and skilled Staff Data Engineer to join our dynamic team. In this role, you’ll play a crucial part as the technical bridge between our data engineering team and the product organization. Reporting to the Data Engineering department, you’ll have the exciting responsibility of executing strategies that align with our Technology Services standards, ensuring that our data engineering solutions effectively drive product development and foster innovation. You will get to collaborate closely with the Data Solutions Product Manager to identify key projects that contribute to our overall goals and achievements. One of your primary functions will be engaging with stakeholders, including product teams and business partners, to clarify requirements and assess business needs. Additionally, you will analyze data to inform decisions on system architecture and tooling. Your coding expertise will shine as you implement maintainable and reusable code while mentoring others informally. You'll also be responsible for conducting rigorous testing, from unit tests to user acceptance tests, ensuring top-notch functionality and security. Moreover, you will build tools and create automation processes that optimize our data management and access. As we embrace a hybrid work model, you will have the flexibility to split your time between the office and remote work, allowing for a balanced work-life environment. We’re excited to see how you can contribute to Mentor’s ongoing success as we continue to pave the way in product development with data-driven insights.

Frequently Asked Questions (FAQs) for Staff Data Engineer Role at Visa
What are the key responsibilities of a Staff Data Engineer at Mentor?

As a Staff Data Engineer at Mentor, your primary responsibilities will include serving as a vital link between the Data Engineering team and the Product organization, executing data engineering strategies, and collaborating with the Data Solutions Product Manager on innovative projects. You'll engage with stakeholders to gather requirements, analyze data for database design, and implement maintainable code while conducting thorough testing to ensure data quality and pipeline performance.

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What qualifications are required for the Staff Data Engineer role at Mentor?

Candidates for the Staff Data Engineer position at Mentor should possess a strong background in data engineering, proficient coding skills, and an understanding of database design. Experience with data analysis, system architecture, and agile methodologies is crucial. Additionally, familiarity with automation processes and a proactive approach towards problem-solving will greatly benefit those in this role.

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How does the Staff Data Engineer contribute to product development at Mentor?

The Staff Data Engineer at Mentor plays a critical role in product development by ensuring that data engineering solutions are seamlessly integrated into product initiatives. You will help foster a data-driven culture by working closely with product teams to identify needs, provide actionable insights, and implement technical solutions that enhance product functionality and drive innovation.

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What does the hybrid work model look like for a Staff Data Engineer at Mentor?

The hybrid work model for a Staff Data Engineer at Mentor allows you to alternate between remote and office work. Employees are generally expected to work from the office 2-3 set days per week, based on business needs. This flexibility encourages a balance between collaborative in-person efforts and focused remote work.

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What tools and technologies should a Staff Data Engineer at Mentor be familiar with?

A Staff Data Engineer at Mentor should be familiar with a variety of tools and technologies including data processing frameworks, cloud services, and database management systems. Knowledge of programming languages such as Python or Java, along with experience in building automation processes and data pipelines, will be essential to effectively execute the responsibilities of this role.

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Common Interview Questions for Staff Data Engineer
Can you describe your experience with data pipeline development in the Staff Data Engineer role?

In answering this question, focus on specific projects you've undertaken, detailing the technologies you used, the challenges you faced, and the outcomes of your efforts. Be sure to highlight your role in collaborating with other teams and how your contributions improved the data processing workflows.

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How do you ensure data quality in your engineering projects?

When discussing data quality, emphasize your approach to implementing data validation processes, automated testing, and monitoring systems. Mention specific tools or methodologies you've used to maintain high data quality standards, showcasing how these practices benefited the projects you've worked on.

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What coding patterns do you prefer when developing data solutions?

This question is about assessing your coding practices. Discuss your familiarity with design patterns that enhance code maintainability and reusability. Provide examples of coding patterns you’ve employed in past projects and explain how they contributed to code clarity and performance.

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Describe a challenging project you worked on. How did you overcome the obstacles?

Use the STAR method (Situation, Task, Action, Result) to describe a challenging project. Detail what made it difficult, the actions you took to solve the issues, and the final impact your solutions had on the project. This helps demonstrate your problem-solving skills and resilience.

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How do you approach collaboration with non-technical teams like product managers?

Highlight your interpersonal skills by discussing how you communicate complex technical concepts in a way that's accessible to non-technical team members. Describe experiences where collaboration led to successful project outcomes and how you maintained an emphasis on mutual understanding.

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What frameworks or tools do you use for data engineering?

Be specific about the frameworks and tools you've used, such as Apache Spark, Hadoop, or any cloud-based services like AWS or Azure. Discuss the context in which you used these tools, their functionalities, and how they enabled you to successfully complete projects.

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How do you keep up with advancements in data engineering?

Discuss your strategies for staying current, such as following industry news, attending webinars, engaging with online communities, or completing relevant courses. This shows your commitment to continuous learning and adapting to new technologies in the fast-evolving field of data engineering.

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Explain your experience with data modeling.

In your response, share specific data modeling projects you've completed, including the methodologies and tools you employed. Describe how your data models supported business objectives and enhanced data accessibility and analysis for various applications.

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What role do you think automation plays in data engineering?

Emphasize the importance of automation in improving efficiency and reducing human error in data processes. Provide examples of how you’ve implemented automation in your previous roles, highlighting the benefits it brought to data management and processing.

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What do you consider when evaluating the success of a data engineering project?

Define metrics for success such as data reliability, performance improvements, scalability, and user satisfaction. Discuss how you assess these elements post-implementation, ensuring that the project meets both technical and business success criteria.

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Visa Inc. operates as a payments technology company worldwide. The company facilitates commerce through the transfer of value and information among consumers, merchants, financial institutions, businesses, strategic partners, and government entiti...

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Full-time, hybrid
DATE POSTED
April 4, 2025

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