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Data Engineer, Remote

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to accomplish their financial goals and help them save time and money.

We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments.

We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.

Job Description

Experian Marketing Services is looking for a Data Engineer to join our global engineering team. This role will report to the Director of Engineering. You will build advanced software systems. We face daily challenges that are both unique and engaging, while processing data at petabyte scale. That is over one trillion data points in any given 60-day period — with consumer privacy and data security at the heart of everything we do. We use Scala, in combination with large-scale data processing on Google Cloud, AWS, machine learning, and open-source technologies, to build our suite of Graph products.

 

Responsibilities

  • Enhance the team by contributing to the architecture, design, and implementation of EMS systems
  • Contribute to technical projects. Working with Tech Leads to design, build, and deliver successful, complex technical projects
  • Be on call if required and to accommodate east coast time zone
  • Collaborate with other engineering, product, and data science teams to ensure we're building the best products

Qualifications

  • 3+ years of experience making contributions in the form of code
  • Experience with algorithms and data structures in knowing when to apply them. Familiarity with Scala, Java, or Python is a plus.
  • Experience working with high-scale systems: realtime and batch
  • Interested in data engineering to develop ingestion engines, ETL pipelines, and organizing the data to expose it in a consumable format
  • Passionate about helping your teammates grow by providing insightful code reviews and feedback
  • #LI-Remote

Skills & Experience

  • Google Cloud Platform (GCP) or AWS
  • Scala, Java, Python, sbt, cats, http5s, fs2
  • Airflow, Kubernetes
  • Google Dataflow/Beam, Spark, SQL, BigQuery or similar data warehouse

Additional Information

Benefits/Perks:

  • Great compensation package and bonus plan of 15%
  • Core benefits including medical, dental, vision, and matching 401K
  • Flexible work environment, ability to work remotely, hybrid, or in-office
  • Flexible time off including volunteer time off, vacation, sick, and 12-paid holidays

Our uniqueness is that we celebrate yours. Experian's culture and people are important differentiators. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; World's Best Workplaces™ 2024 (Fortune Top 25), Great Place To Work™ in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site to understand why.

Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

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Average salary estimate

$105000 / YEARLY (est.)
min
max
$90000K
$120000K

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 Data Engineer, Remote, Experian

Are you ready to take your data engineering skills to the next level? Experian Marketing Services is on the lookout for a passionate Data Engineer, specifically to join our innovative global engineering team in a fully remote position! At Experian, we empower countless businesses and individuals by harnessing the power of data and technology. As a Data Engineer, you will play a vital role in building sophisticated software systems that handle massive data volumes at petabyte scale, all while ensuring consumer privacy and data security. You’ll be diving into the challenges of processing over one trillion data points, collaborating with talented engineering and product teams to design and implement advanced systems. With a foundation in Scala, AWS, Google Cloud, and an array of exciting technologies, we can’t wait to see how you can enhance our architecture and contribute to the success of our complex technical projects. If you have a couple of years under your belt, are familiar with algorithms and data structures, have experience with high-scale systems, and are keen to develop ETL pipelines and ingestion engines, we would love to hear from you! Join us at Experian, where not only will you have a chance to grow your technical skills, but you will also benefit from a flexible work environment, a generous compensation package, and a culture that celebrates diversity and collaboration. Ready to make a difference? Let’s do it together!

Frequently Asked Questions (FAQs) for Data Engineer, Remote Role at Experian
What are the main responsibilities of a Data Engineer at Experian?

The Data Engineer at Experian plays a pivotal role in enhancing the architecture, design, and implementation of our Engineering Management Systems (EMS). Responsibilities include contributing to technical projects, collaborating with Tech Leads on complex projects, and ensuring our data engineering efforts result in high-quality ingestion engines and efficient ETL pipelines.

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What qualifications are needed to become a Data Engineer at Experian?

To qualify for the Data Engineer role at Experian, candidates should have at least 3 years of experience in software development, proficiency in programming languages like Scala, Java, or Python, and a solid understanding of algorithms and data structures. Familiarity with cloud technologies such as Google Cloud Platform (GCP) or AWS is also beneficial.

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How does Experian support the professional growth of its Data Engineers?

Experian prioritizes professional development by encouraging Data Engineers to participate in insightful code reviews, offering constructive feedback, and collaborating with cross-functional teams. The company also provides a flexible work environment that supports personal and career growth.

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What technologies does a Data Engineer at Experian work with?

Data Engineers at Experian use a diverse set of technologies, including Scala, Apache Airflow, Kubernetes, Google Dataflow, Spark, and SQL. Familiarity with these technologies is crucial, as they facilitate the processing and analysis of large-scale data.

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Is the Data Engineer position at Experian fully remote?

Yes! The Data Engineer role at Experian is fully remote, allowing professionals to work from anywhere in the United States, offering flexibility and work-life balance.

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Common Interview Questions for Data Engineer, Remote
Can you explain your experience with data structures and when to use them?

When answering this question, detail specific scenarios from past projects where you’ve utilized various data structures effectively. Focus on how the choice of data structure impacted performance and scalability.

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Describe a complex technical project you’ve worked on. What was your role?

Provide a comprehensive overview of the project - its challenges, your contributions, and any specific technologies you employed. Highlight your collaboration with teammates and how your input led to the project's success.

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How do you approach building ETL pipelines?

Discuss your steps, including requirements gathering, selecting tools, and testing. Reflect on a project where you implemented an ETL pipeline successfully and the lessons you learned.

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What experience do you have with cloud platforms like GCP or AWS?

Elaborate on your hands-on experience with GCP or AWS. Include specific services you've used, such as Dataflow or EC2, and how they've benefitted your previous projects.

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How do you ensure data integrity and security in your projects?

Share your strategies for maintaining data integrity and security, such as implementing encryption, data validation, and conforming to privacy standards. Provide examples of your vigilance in these areas.

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What programming languages are you most comfortable with, and why?

Be honest about your strong suits, like Scala, Java, or Python. Explain your preference based on the contexts you've coded in and the projects where those languages were most applicable.

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Have you ever faced a challenge while processing data at scale? How did you handle it?

Share a real-life example of a scalability challenge and detail the steps you took to resolve it. Focus on problem-solving techniques and any tools that you found particularly effective.

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What role do you think collaboration plays in a Data Engineering team?

Discuss the importance of teamwork in Data Engineering, emphasizing shared knowledge, diverse perspectives, and successful project outcomes stemming from collaboration with engineers and other departments.

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Can you explain the data pipeline architecture you’ve designed in the past?

Walking through your architectural design, cover components like data ingestion, storage, processing, and visualization. Emphasize your reasoning behind technology choices and design decisions.

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What do you do when you encounter a data anomaly?

Outline your systematic approach to diagnosing and resolving data issues, including validation strategies, testing methodologies, and how you communicate findings to stakeholders.

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Full-time, remote
DATE POSTED
March 28, 2025

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