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Enterpriser Data Lake – is a newly formed organization under the Technology Platforms & Experience organization. Its vision is to be the single authoritative source of data for all PayPal and enable our internal and external customers with enterprise product data at lower cost and high quality. To see this vision through, the team will be building this platform on the cloud in close partnership with several key stakeholders across PayPal Inc including product, finance, risk, compliance, governance and strategy teams. We are looking for people with a passion and curiosity to solve customer problems with data.
We are seeking highly talented and experienced hands-on engineering talent to enable the controls and governance platform components of the enterprise data lake. The candidate must have strong technical skills and experience building large scale data platforms and have the ability to abstract common application patterns as services and frameworks — think platform first to enable our customer of the platform with secure and seamless access to high quality data.
Responsibilities Include:
As a lead software Engineer, lead a component design/development, work with the development and big data teams to design scalable big data platform framework and services.
Identify, design and build automated test frameworks to enable validation of the solution for scalability, consistency, accuracy and repeatability.
Ability to prototype ideas and demonstrate pros and cons and make recommendations using demos and slides.
Ability to use data to drive products and decisions. Be able to explain complex technical concepts to management, product managers, support, and other engineers.
Collaborate with management to define and set standards for engineering rigor and help cultivate the culture in the team.
Contribute to innovation with a passion for delivering leading edge Big Data Lake solutions to our customers.
Ability to assess new technologies and make pragmatic choices that points towards our long-term vision.
Pro-active response in identifying and troubleshooting integration or technical issues.
Great verbal, communication and follow through skills.
Job Qualifications:
Master’s degree or equivalent in Computer Science or related field with a minimum of 10+ years of hands-on software engineering experience.
8+ years of experience building large scalable and reliable enterprise technology platforms using Big Data open-source technologies such as Hadoop, HBase, Spark, Kafka and Elastic Search / Solr
At least 5 years’ experience in large scale, production, server-side development on the JVM, Scala, Python and/or GO
3+ years of experience with SQL and relational database systems like Oracle, PostgreSQL or MySQL is preferable
2+ years of experience with at least one of the cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS) or Azure
Experience with cloud native technologies
Experience with building RESTful API’s is preferred.
Experience with business rules management systems (e.g., drools) is preferred.
Hands on experience with engineering developer tools-GIT, Dockerization, Continuous Integration frameworks , etc.
Knowledge of industry standard best practices (e.g., SOX, DAMA, CCPA , etc.) with respect to security, privacy and quality.
Passionate about Software Quality and ensuring that Secure Development Lifecycle best practices are followed
Ability to adapt to new development environments, changing business requirements and learning new systems highly desired.
Excellent verbal and written communication skills.
Good team player, able to effectively work across multiple teams on solutions that have complex dependencies and requirements in a fast-paced environment
Experience working in agile software development model preferred.
Experience with Tableau or other visualization tools is a plus.
Experience with open-source contribution is a plus
Nice-to-haves:
Prior experience building/operating large-scale distributed systems and services
Contributions to open-source projects preferably in the platform space
Experience in handling petabyte scale data