- Страна
- США
- Зарплата
- 200 000 $ – 275 000 $
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Staff Software Engineer, Backend (Streaming)
Высокая оценка обусловлена прозрачной структурой оплаты, отличным пакетом льгот (100% страховка, стипендии) и возможностью работать удаленно в ведущей финтех-компании. Роль предлагает значительное влияние на архитектуру всей компании.
Сложность вакансии
Роль уровня Staff требует не только глубочайшей технической экспертизы в Kafka и Flink, но и способности определять стратегию развития инфраструктуры данных. Ожидается опыт работы с высоконагруженными системами (миллионы транзакций) и навыки менторства.
Анализ зарплаты
Предлагаемая зарплата ($200k - $275k) полностью соответствует рыночным ожиданиям для позиции Staff Engineer в США, особенно учитывая дополнительные бонусы и опционы.
Сопроводительное письмо
I am writing to express my strong interest in the Staff Software Engineer (Streaming) position at Affirm. With over 8 years of experience in building large-scale production systems and a deep specialization in Apache Kafka and Flink, I have consistently delivered robust data infrastructure that powers critical business applications. My background in designing high-throughput, low-latency pipelines aligns perfectly with Affirm’s mission to provide transparent and flexible credit solutions.
In my previous roles, I have led technical projects involving CDC tools, Apache Iceberg, and complex event-driven architectures. I am particularly drawn to this role because of the Streaming team's central role in Affirm's data strategy and the opportunity to work with a modern stack including Snowflake and Databricks. I am confident that my expertise in stream processing and my collaborative approach to mentoring and stakeholder management will allow me to make immediate contributions to your Data Platform's reliability and efficiency.
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Откликнитесь в affirm уже сейчас
Присоединяйтесь к Affirm, чтобы создавать будущее честного кредитования и масштабировать стриминговые системы мирового уровня!
Описание вакансии
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
The Streaming team at Affirm forms the backbone of several online and offline workloads at Affirm and drives the strategy for event driven architecture, stream processing, replication, data exploration, discovery and validation. We leverage existing open source technologies like Kafka, Flink, Spark and also build our own as needed to design and build real-time data processing pipelines that handle massive volumes of data across our distributed systems.
As a member of our team you would spend time identifying and executing on new use cases of streaming data infrastructure and frameworks , designing and scaling our existing infrastructure and working closely with other teams to promote the optimal use of data at the company. This role requires deep expertise in stream processing technologies and a passion for building scalable, fault-tolerant data infrastructure.
Key Responsibilities
- Design and build data infrastructure systems, services and tools to handle new Affirm products and business requirements that securely scale over millions of users and their transactions.
- Build and optimize high-throughput, low-latency data pipelines for critical business applications
- Build frameworks and services which will be used by other engineering teams at Affirm to manage billions of dollars in loans and power customer experiences.
- Help support the Streaming team that forms the backbone for several online and offline workloads at affirm.
- Improve the reliability and efficiency of the Data Platform at scale and high availability.
- Collaborate with other teams at Affirm, including ML and Analytics, to deliver streaming solutions based on various use-cases and best practices
- Monitor, troubleshoot, and maintain production streaming systems, ensuring high availability and reliability of data systems
What We Look For:
- 8+ years of industry experience in building large scale production systems.
- Strong hands on experience with Apache Kafka or similar streaming solutions for large-scale event streaming and message queueing
- Experience building and operating robust and highly available infrastructure.
- Experience working with data platforms like Snowflake, Glue or Databricks is a plus
- Experience with Confluent Platform (Schema Registry, Tableflow) is a strong plus.
- Expertise with at least one stream processing frameworks like Spark, Samza, Flink, Beam etc.
- Hands-on experience with Kafka Connect, and Kafka Schema Registry components, understanding its underlying functionality and implementation
- Solid programming skills in Python, Java or Kotlin.
- Experience with Apache Iceberg table format, CDC (Change Data Capture) tools is a strong plus
- Knowledge of Relational and NoSQL databases is a plus.
- Experience leading technical projects and mentoring junior engineers.
- Exceptionally collaborative with a history of delivering complex technical projects and working closely with stakeholders.
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field
Base Pay Grade - P
Equity Grade - 13
Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.
Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $225,000 - $275,000
USA base pay range (all other U.S. states) per year: $200,000 - $250,000
#LI-Remote
Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.
We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
- Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
- Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
- Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge
- ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount
We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
[For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.
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Навыки
- Apache Kafka
- Apache Flink
- Apache Spark
- Python
- Java
- Kotlin
- Apache Iceberg
- Snowflake
- Databricks
- CDC
- NoSQL
- Relational Databases
- Kafka Connect
- Schema Registry
Возможные вопросы на собеседовании
Проверка глубины знаний основного инструмента команды.
Расскажите о наиболее сложной проблеме производительности или надежности в Kafka, с которой вы столкнулись, и как вы её решили?
Affirm работает с финансовыми данными, где важна точность.
Как вы обеспечиваете семантику 'exactly-once' в распределенных стриминговых пайплайнах при использовании Flink или Spark Streaming?
Позиция Staff подразумевает проектирование систем.
Как бы вы спроектировали систему репликации данных в реальном времени между различными регионами с учетом минимизации задержек и обеспечения консистентности?
В описании упомянуты Iceberg и CDC.
Каковы преимущества и недостатки использования Apache Iceberg в качестве формата таблицы для стриминговых данных по сравнению с традиционными подходами?
Оценка лидерских качеств и влияния на команду.
Опишите случай, когда вам пришлось убеждать стейкхолдеров в необходимости крупного изменения в архитектуре данных. Как вы выстраивали аргументацию?
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- Страна
- США
- Зарплата
- 200 000 $ – 275 000 $