Analytics Engineer

Remote from: USA
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 18 Jun 2026
Apply before: 18 Jul 2026
Experience level: Midweight
Views / Applies: 12 / 1

Experian unlocks the power of data to create opportunities for consumers, businesses and society.

Actively Hiring

AI Summary

Experian is hiring an Analytics Engineer for their Fraud Analytics team to build and maintain scalable data pipelines, platforms, and infrastructure supporting fraud attribute development, model building, and deployment. The role requires expertise in Python, AWS, CI/CD, and machine learning workflows, with a focus on reliability and usability. The ideal candidate thrives in ambiguity, has an impact-focused mindset, and collaborates with data scientists and engineers. This position offers flexible work arrangements, competitive compensation, and benefits including medical, dental, vision, and 401K matching. Experian is a global data and technology company operating across financial services, healthcare, automotive, and more, with a strong emphasis on innovation and diversity.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires a broad skill set spanning data engineering, infrastructure, and machine learning, as well as the ability to navigate ambiguity and work across teams, making it moderately difficult.

Salary Analysis

AI Insight The salary information was not provided in the job listing; however, based on US market data for Analytics Engineers, the estimated median salary is $135,000, which is competitive for a role requiring 3+ years of experience and advanced technical skills. The offered compensation package likely includes bonuses and benefits, enhancing total value.

Key Skills

Python AWS PySpark Data Pipelines Machine Learning CI/CD Infrastructure as Code Fraud Analytics Airflow Object-Oriented Programming

Cover Letter Sample

I am excited to apply for the Analytics Engineer position at Experian. With a strong background in data engineering and machine learning, I have built scalable Python-based pipelines and deployed production systems on AWS. My experience includes working closely with data scientists to optimize fraud analytics workflows, ensuring reliability and efficiency.

At my previous role, I developed CI/CD pipelines and infrastructure-as-code solutions that improved deployment frequency by 40%. I am passionate about creating platforms that empower teams to innovate, and I am eager to bring my skills in PySpark, Airflow, and cloud security to Experian’s Fraud Analytics team.

I thrive in collaborative environments and am comfortable navigating ambiguity to deliver impact. I am particularly drawn to Experian’s mission of unlocking the power of data and would be honored to contribute to your fraud analytics infrastructure.

Possible Interview Questions

Describe a time you built a scalable data pipeline for analytics. What tools did you use and what challenges did you face?

I developed a pipeline using Python and PySpark to process real-time clickstream data for fraud detection. Key challenges were ensuring data consistency across multiple sources and handling spikes in volume. I implemented idempotent processing and used AWS EMR with auto-scaling to maintain performance.

How do you approach debugging a performance issue in a production data pipeline?

I start by monitoring metrics like throughput and latency, then isolate the bottleneck using logs and profiling tools. For example, when a pipeline slowed due to a shuffle-heavy operation, I optimized the data partitioning and switched to columnar storage, resulting in a 50% improvement.

Explain your experience with CI/CD for data infrastructure. How did you ensure reliability?

I set up CI/CD pipelines using Jenkins and Terraform to automate deployments of AWS resources. To ensure reliability, we implemented automated testing for schema changes and rollback procedures. We also used canary deployments to validate before full rollout.

How would you support a data scientist who needs a new feature for a model? Describe your process.

I would first understand the feature requirements and data sources. Then I'd collaborate to design a scalable ingestion and transformation layer, ensuring the feature is available in the model training environment. I'd also set up monitoring to track data quality and pipeline health.

Tell me about a time you introduced a new technology to improve workflow efficiency. What was the outcome?

I introduced Airflow to replace a cron-based scheduling system for model retraining. This provided better observability, error handling, and parallel execution. The result was a 70% reduction in manual interventions and faster iteration cycles for the data science team.

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.

Job Description

The Fraud Analytics & Commercialization team drives Experian’s fraud analytics business through four integrated functions: pre-sales engagement, scalable and custom solutions, consulting, and operational enablement, with the goal of becoming the industry’s provider-of-choice.

We’re looking for an Analytics Engineer, Fraud Analytics Infrastructure, to join our Fraud Analytics team. As the ideal candidate, you thrive at the intersection of data engineering, infrastructure, and machine learning, and care about the reliability, scalability, and usability of the platforms your colleagues depend on. You’ll work closely with data scientists, engineers, and product partners to build, maintain, and continuously improve the analytics ecosystem that enables fraud attribute development, model building, and model deployment, including identifying opportunities to increase efficiency and stability across the full modeling lifecycle. Core skills include navigating ambiguity, an impact-focused mindset, critical thinking, and an eagerness to collaborate across teams. You are curious about the latest tools and AI solutions, will evaluate their potential, and help bring the best of them into the team’s workflows. Candidates who have taken an unconventional path and demonstrated the curiosity to figure things out without a blueprint will find this role and team to be a good fit.

You will report to the Data Modeling Director.

You’ll have the opportunity to:

Build scalable Python-based data pipelines and backend services for analytics workflows.
Design software systems using object-oriented programming and sound engineering practices.
Create and support platforms that allow analytics development, model training, and model deployment.
Implement and maintain CI/CD pipelines and infrastructure-as-code solutions for automated deployments.
Manage cloud and on-premises analytics environments, including AWS infrastructure and security controls.
Monitor, troubleshoot, and improve data pipelines, platform performance, and system reliability.
Support machine learning and fraud modeling workflows, including feature engineering and model deployment.
Implement new technologies, including AI-based solutions, to improve platform efficiency and stability.

Qualifications

3+ years of experience in data science, analytics, data engineering, or a related field.
Bachelor’s or advanced degree in Statistics, Applied Mathematics, Econometrics, or another quantitative field; equivalent experience considered.
Experience developing applications and data pipelines using Python, including PySpark, Polars, NumPy, and Pandas.
Familiarity with Java and object-oriented programming concepts.
Experience building, deploying, and supporting production systems and data platforms.
Experience with AWS services such as EC2, EMR, and Airflow, including cloud security best practices.
Experience with machine learning workflows and analytics model development environments.
Experience with CI/CD processes, Infrastructure as Code, containerization tools, and UNIX/Linux environments.

Additional Information

Benefits/Perks:

Great compensation package and bonus plan.
Core benefits including medical, dental, vision, and matching 401K.
Flexible work environment, ability to work remote, hybrid or in-office.
Flexible time off including volunteer time off, vacation, sick and 12-paid holidays.
Explore all our exciting benefits here: https://myexperianbenefits.com/.

Our uniqueness is that we celebrate yours. Experian’s people first, inclusive and purpose driven culture is multi award-winning; World’s Best Workplaces™ 2025 (Fortune Global Top 25), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why.

Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay range for this position is listed above. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. You will be also eligible for a variable pay opportunity.

Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

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