Senior Data Engineering Analyst

Employment Type: Full-time Location Type: 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.

Position Overview

Experian is looking for a Senior Data Engineering Analyst to join Experian's Commercial Data Sciences (CDS) team. This is a unique opportunity to get involved in the CDS Data Solutions team from the ground level by developing new internal and external data pipelines while supporting the creation and auditing of attributes/features/models from multiple data sources. You will conduct exploratory evaluations of data in a big data environment and identify the latest data sources to guide commercial insights. You will report into the Data Solutions Manager.

Responsibilities

• Partner with the technology team to create new data pipelines
• Develop dashboards and reports, using data visualization and other tools
• Evaluate new data sources and assess their value-added when blending them with current data sources
• Write efficient scalable code and automated repeatable processes
• Create, audit, and document attributes/features
• Create Proof-of-Concept (POC) models
• Produce reports following quality control procedures to document analysis and findings
• Work with teams to resolve internal and external client questions

Qualifications

• Bachelor of Science or similar degree in Data Science, Computer Science, Information Management, Engineering, Economics, Statistics, or another related field
• 3+ years of relevant working experience
• Experience working with Python, SQL, SAS, Hive, UNIX, or Tableau
• Experience in big data environments like AWS, PySpark, HUE, or Scala
• Experience with consumer and commercial credit data
• Experience with feature engineering and risk modeling
• #LI-Remote

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://yourexperianbenefits.com/cand-index.html

Experian Culture & Values:

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.

Equal Opportunity Employer:

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, color, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.