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Senior Big Data Engineer

Оценка ИИ

Отличная позиция в стабильной компании-единороге с сильной инженерной культурой и современным стеком (Spark, Databricks, K8s). Высокая значимость роли для бизнеса и работа с действительно большими данными делают вакансию очень привлекательной для опытных инженеров.


Вакансия из Quick Offer Global, списка международных компаний
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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена требованиями к глубоким знаниям Apache Spark, опытом работы с высоконагруженными системами (миллиарды транзакций) и необходимостью понимания специфики MLOps. Роль требует не только инженерных навыков, но и способности консультировать Data Science команды.

Анализ зарплаты

Медиана135 000 $
Рынок110 000 $ – 160 000 $
Оценка ИИ

Зарплата для Senior Data Engineer в Тель-Авиве является одной из самых высоких в регионе. Указанный диапазон соответствует рыночным ожиданиям для опытных специалистов в продуктовых компаниях уровня Forter.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Big Data Engineer position within the ML Platform team at Forter. With over 5 years of experience in building large-scale distributed systems and a deep expertise in Apache Spark and Python, I have consistently focused on optimizing data pipelines for high-throughput environments. My background in backend development and complex data engineering patterns aligns perfectly with Forter's mission to provide reliable, feature-rich data for real-time fraud detection.

In my previous roles, I have successfully tackled challenges related to data reproducibility and feature store optimization, which are central to the responsibilities of this position. I am particularly drawn to Forter because of your commitment to precision at scale and the opportunity to act as a technical consultant for data scientists. I am eager to bring my skills in Spark, Databricks, and cloud infrastructure to help Forter continue its impressive growth and innovation in the e-commerce trust space.

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Откликнитесь в forter уже сейчас

Присоединяйтесь к команде Forter и создавайте масштабируемые ML-системы, которые защищают миллиарды транзакций в реальном времени!

Описание вакансии

About the role:

Forter is looking for a Senior Big Data Engineer to join our growing ML Platform team. This is a great opportunity for an experienced Data Engineer who wants to apply their skills to the world of Machine Learning. Together, we’ll provide the high-throughput tools and distributed systems needed to process billions of data points, ensuring that our models have the feature-rich, reliable data they need to perform in real-time.

ML is central to Forter’s work. It enables us to process billions of dollars worth of e-commerce transactions and identify fraud rings. Precision at scale is crucial—our models depend on engineered features derived from massive datasets. As the volume of data and the complexity of these models grow, we need senior engineers who can solve the "Big Data" challenges inherent in modern MLOps.

Why should you join us?

You’ll be part of a highly proficient engineering team that is the focal point for all ML engineering activity. This role is perfect for a Big Data specialist who wants to own the data-intensive side of ML—building the pipelines and infrastructure that transform raw global traffic into actionable intelligence.

What you will be doing:

  • Designing and building the data-heavy components of our ML infrastructure, allowing Forter’s models to make billions of real-time decisions every year.
  • Building distributed data processing pipelines that power model training, feature engineering, and offline research at scale.
  • Optimizing the "Feature Store" concept—ensuring that the data used for training matches the data used in production for real-time serving.
  • Acting as a technical consultant to data scientists and researchers, helping them navigate massive datasets and providing the Spark-based tooling they need to iterate faster.
  • Expanding our ML data infrastructure to be scalable and efficient, focusing on the performance of data ingestion and transformation for diverse model types.
  • Improving MLOps standards by ensuring data reproducibility, lineage, and quality across the entire model lifecycle.

Who are you?

  • 5+ years of experience with large-scale data processing, with deep expertise in Apache Spark.
  • 5+ years developing complex software with at least one general-purpose language (preferably Python or Scala).
  • Backend and server-side development experience of complex, highly scalable systems.
  • Strong understanding of Data Engineering patterns (Partitioning, Sharding, Schema evolution) and how they apply to model performance.
  • Interest in Machine Learning concepts—you don't need to be a researcher, but you should understand how data flows into a model.
  • Experience working with public clouds (AWS / GCP / Azure).
  • Fluent in written and spoken English.

It’d be really cool if you also:

  • Are familiar with Databricks or Airflow for orchestrating complex ML workflows.
  • Are comfortable in a containerized environment (Kubernetes/Docker).
  • Have experience with low-latency, real-time data serving or streaming (Kafka).

About us:

Digital commerce is built on trust.At every point along the eCommerce journey, businesses must make a critical decision: Can I trust this customer? Answering this simple question accurately and instantly is powerful—it can accelerate revenue growth and strengthen a company’s connection with its customers. How do we do it? Forter was founded on the insight that it's not about what is being purchased, nor where— but who is behind the interaction.

The Forter Decision Engine finds patterns across more than one billion identities in our dataset. We isolate fraudsters and protect customers—ensuring everyone gets the experience they deserve.  Given that trust is central to how we operate, Forter is very much driven by a defined set of values. We attract remarkable talent and have retention and engagement levels that are well above benchmarks. We’re meticulous about strengthening our culture as we grow and ensuring this is an environment where people can have outsized impact.

Trust is backed by data –  Forter is a recipient of over 10 workplace and innovation awards, including:

Life as a Forterian:

We are a team of over 500 Forterians spread across 3 different continents. Since 2013, we've raised $525 million from investors such as Tiger Global, Bessemer, Sequoia Capital, March Capital and Salesforce Ventures. We're on a mission to bring trust to global digital commerce so that companies like Nordstrom, Priceline, Instacart and ASOS can block fraud, drive revenue and improve customer experience.

At Forter, we believe unique people create unique ideas, and valuable experience comes in many forms. So, even if your background doesn't match everything we have listed in the job description, we still encourage you to apply and tell us why your skills and values could be an asset to us. By welcoming different perspectives, we grow together as humans and as a company.

Forter is an Equal Employment Opportunity employer that will consider all qualified applicants, regardless of race, color, religion, gender, sexual orientation, marital status, gender identity or expression, national origin, genetics, age, disability status, protected veteran status, or any other characteristic protected by applicable law.

If you need assistance or an accommodation due to a disability, please email us at interviewaccommodation@forter.com. This information will be treated as confidential and used only for the purpose of determining an appropriate accommodation for the interview process.

Forter's Applicant Privacy Policy

Forter's Applicant Privacy Policy

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Навыки

  • Apache Spark
  • Python
  • Scala
  • AWS
  • Google Cloud Platform
  • Azure
  • Databricks
  • Airflow
  • Kubernetes
  • Docker
  • Kafka
  • Machine Learning
  • MLOps
  • Backend Development

Возможные вопросы на собеседовании

Проверка глубоких знаний Spark, заявленных в требованиях.

Расскажите о наиболее сложной проблеме производительности Spark, с которой вы столкнулись (например, skew данных), и как вы её решили?

Вакансия делает акцент на Feature Store и консистентности данных.

Как вы обеспечиваете идентичность данных (training-serving skew) при расчете признаков для обучения и для инференса в реальном времени?

Важно понимать опыт кандидата в проектировании надежных систем.

Как вы подходите к обеспечению воспроизводимости данных и отслеживанию их происхождения (lineage) в сложных ML-пайплайнах?

Проверка навыков работы с облачной инфраструктурой и масштабированием.

Опишите ваш опыт оптимизации затрат и производительности при работе с большими данными в публичных облаках (AWS/GCP).

Роль предполагает взаимодействие с Data Scientists.

Как вы обычно выстраиваете процесс взаимодействия с исследователями данных, чтобы помочь им быстрее итерироваться с использованием Spark?

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