Tenjin, a mobile marketing attribution platform, is seeking a Senior Backend Engineer specializing in ad attribution. The role involves building and maintaining scalable, high-performance systems using Ruby and Go to process large datasets and accurately attribute mobile app installs to ad campaigns. This includes working on their core attribution logic, fraud detection, and reporting features. The ideal candidate has strong backend experience, particularly with Ruby and Go, and a deep understanding of ad tech and attribution.
Tenjin, a mobile marketing attribution platform and a Y Combinator Summer 2014 alumnus, is actively seeking a highly experienced Senior Backend Engineer specializing in ad attribution to join their team. This role offers the opportunity to work on the core attribution logic that powers Tenjin’s platform, which processes immense volumes of data to accurately attribute mobile app installs to their respective advertising sources. The ideal candidate will be a proficient programmer with substantial experience in both Ruby and Go programming languages. They will be deeply involved in developing and maintaining the complex systems that handle the ingestion, processing, and analysis of attribution data, including click and install logs. This engineer will be responsible for ensuring the accuracy and efficiency of Tenjin's attribution pipeline, which is crucial for providing valuable insights to mobile app marketers. The role involves tackling challenging technical problems related to scaling data processing infrastructure, optimizing query performance, and maintaining data integrity at scale. The successful candidate will contribute directly to the improvement and evolution of Tenjin’s core technology, playing a pivotal role in the company's continued growth and success in the mobile marketing analytics space. They will work within a collaborative team environment and have the opportunity to make a significant impact on a product utilized by a large number of mobile app developers and marketers.
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https://news.ycombinator.com/item?id=43781663
HN commenters discuss Tenjin's tech stack choices, particularly using Ruby and Go together. Some question the combination, expressing concerns about Ruby's performance in a data-intensive ad attribution environment. Others defend the choice, suggesting Ruby might be used for less performance-critical tasks or that Tenjin might be transitioning to Go. A few commenters focus on the remote work aspect, viewing it positively. Some also note the competitive salary range. Overall, the discussion revolves around the suitability of Ruby and Go for ad attribution, remote work opportunities, and the advertised salary.
The Hacker News post discussing the Tenjin job posting for a Senior Ad Attribution Engineer has generated several comments, largely focusing on the complexities and nuances of ad attribution.
One commenter highlights the inherent difficulty of ad attribution, particularly in a mobile context where accurately tracking user journeys across different platforms and devices poses a significant challenge. They emphasize that determining the "true" source of a conversion is often ambiguous and subject to various interpretations. This ambiguity can lead to disagreements between advertisers and attribution providers, making the field quite challenging.
Another commenter points out the importance of probabilistic attribution models in addressing these complexities, suggesting that relying solely on last-click attribution is an oversimplification. They allude to the need for more sophisticated statistical approaches to distribute credit across multiple touchpoints in a user's conversion path.
Further discussion delves into the technical aspects of ad attribution, including dealing with issues like click spamming and fraud. One commenter raises the challenge of handling situations where multiple ad networks claim credit for the same conversion, highlighting the need for robust validation and deduplication mechanisms.
The conversation also touches upon the role of privacy in ad attribution. One commenter mentions the increasing restrictions on tracking user behavior due to privacy regulations and platform policies (like Apple's App Tracking Transparency), adding another layer of complexity to the attribution process. This raises questions about the future of ad attribution and the need for innovative solutions that respect user privacy.
Finally, a commenter notes the specific technologies mentioned in the job posting (Ruby and Go) and speculates on their potential use cases within the ad attribution pipeline. This suggests that candidates with expertise in these languages may be particularly well-suited for the role.
Overall, the comments paint a picture of ad attribution as a technically challenging and constantly evolving field, grappling with issues of accuracy, complexity, and privacy. The discussion provides valuable context for the job posting, offering potential applicants insights into the challenges and opportunities they might encounter in this role.