About GPC Global Technology Center
Established in 1928, Genuine Parts Company is a leading global service provider of automotive and industrial replacement parts and value-added solutions. Our Automotive Parts Group operates across the U.S., Canada, Mexico, Australasia, France, the U.K., Ireland, Germany, Poland, the Netherlands, Belgium, Spain and Portugal. At the same time, our Industrial Parts Group serves customers in the U.S., Canada, Mexico and Australasia. We keep the world moving with a vast network of over 10,700 locations spanning 17 countries, supported by more than 63,000 teammates. Learn more at genpt.com.
Position Purpose
This role will be the Senior Data Quality Engineer of a newly formed AI/ML Engineers team to build the next models and analytics. Working with a highly talented team, you'll play a key role in building and run one of the world’s largest automotive and industrial replacement parts operations.
This is a data quality/engineering role with responsibility of supporting and validating data science initiatives that drive business profitability, increased efficiencies, and improved customer experience. This individual must be an engineer at heart and be comfortable in enabling new technology and being hands on with the execution of the strategy. She/he must exhibit a deep understanding of modern technology stack and agile delivery models, demonstrated focus on customer experience, and must have a proven track record of modernizing technologies.
We are seeking an experienced Senior Data Quality Engineer to join our team. The ideal candidate will have a strong background in devops and cloud technologies. Familiar with retail business and/or search domain is an added advantage. In this role, you will work closely with cross-functional teams to drive insights and recommendations to inform business decisions along with data scientists and data engineers.
Suppose you are passionate about leveraging data to drive business decisions and have a strong background in data operations and cloud technologies. In that case, we encourage you to apply for this exciting opportunity.
Responsibilities
You’ll work on the GPC's Data Science team collaborating with data scientists, AI/ML engineers and product managers to build and augment our AI-driven digital Catalog products. You’ll leverage Python and/or scripting languages to validate data output from ML models, explore and analyze various data sources like catalog, customer engagement, financial reports.
Understand and follow Agile principles, best-practice software design, participate in code reviews, create a maintainable and well-tested codebase with relevant documentation.
3+ years’ experience in software engineering
BS in Mathematics, Statistics, Operations Research, Industrial Engineering, Physics, Computer Science, or other related fields
Strong hands-on programming skills in Python. Knowledge of SQL, Spark/equivalent.
Expertise in running queries against data (preferably with Google BigQuery or SQL)
Experience in designing and running A/B Tests and analyze the Key Metrics
Experience with cloud computing on at least one of the major providers (Google Cloud Platform, AWS, or Azure).
Some experience with Prompt Engineering in QA and Software Testing
Location
Krakow/Hybrid
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GPC conducts its business without regard to sex, race, creed, color, religion, marital status, national origin, citizenship status, age, pregnancy, sexual orientation, gender identity or expression, genetic information, disability, military status, status as a veteran, or any other protected characteristic. GPC's policy is to recruit, hire, train, promote, assign, transfer and terminate employees based on their own ability, achievement, experience and conduct and other legitimate business reasons.
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What We Do
Genuine Parts Company (GPC), founded in 1928, is a global service organization engaged in the distribution of automotive and industrial replacement parts. We serve hundreds of thousands of customers from a network of more than 10,000 locations in 14 countries and have approximately 50,000 employees.
