HP is a Fortune 100 technology company with $58+ Billion in revenue, with over 50,000 employees operating in more than 170 countries around the world. We provide technology and services that help people and companies address their problems and challenges, and realize their possibilities, aspirations and dreams. We apply new thinking and ideas to create simpler, more valuable and trusted experiences with technology, continuously improving the way our customers live and work.
Position background
In the GTM analytics COE our mission is to deliver impact by building machine learning products to optimize pricing and marketing investments and provide guidance to our sales organization.
As a Big Data Engineer, you will be in a unique position to support the development of one of our internal assets. You will work together with the project and asset team to understand the end state in which the data must be delivered and you will model the data using Big Data technologies like Spark.
What you will be doing:
- Create / Maintain ETL pipelines;
- Review and evaluate designs and project activities for compliance with architecture, security and quality guidelines and standards;
- Provide tangible feedback to improve product quality and mitigate failure risk;
- Design data systems/solutions to manage complex data in complex, distributed and massively parallel systems;
- Effectively communicate product architectures, design proposals and negotiate options at senior management level;
- Provide guidance to lower-level team members.
What you will need:
- Bachelor’s in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field;
- 6+ years in a similar role;
- Strong problem-solving skills;
- Agile experience;
- Experience with modern application lifecycle management tools (Git, Visual Studio, Intellij, Code Reviews);
- Proficient in at least one of the following languages: Python, PySpark, Spark, SparkR.
Nice to have:
- Experience with Databricks;
- Experience with SQL & NoSQL databases (PostgreSQL, MongoDB, Elastic Search).