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Staff Data Engineer
Высокий балл за сильный бренд компании, работу с огромными масштабами данных и отличный социальный пакет (Lab Days, бюджет на технику, прозрачный рост). Позиция Staff дает реальное влияние на продукт.
Сложность вакансии
Роль уровня Staff подразумевает не только глубокие технические знания Spark и Kafka, но и лидерство в архитектуре, менторство и решение сложных бизнес-задач в условиях неопределенности. Ожидается опыт работы с AdTech-масштабами данных (миллионы событий в секунду).
Анализ зарплаты
Зарплата для Staff Data Engineer в Бангалоре значительно выше средней по рынку Индии. Указанный диапазон отражает компенсацию в топовых продуктовых компаниях (Tier-1), включая базовую часть и бонусы.
Сопроводительное письмо
I am writing to express my strong interest in the Staff Data Engineer position at Truecaller. With extensive experience in architecting high-throughput data pipelines and a deep proficiency in Spark, Kafka, and BigQuery, I am confident in my ability to drive the technical vision for your data platforms. My background in leading cross-functional initiatives and operationalizing ML models aligns perfectly with Truecaller's mission to build safer and more efficient communication tools.
Throughout my career, I have specialized in transforming ambiguous business requirements into robust technical roadmaps. I am particularly drawn to Truecaller's 'Lab Days' and the entrepreneurial spirit that encourages bold ideas. I am eager to bring my expertise in Python, Scala, and Airflow to your team in Bangalore, helping to elevate engineering standards and mentor the next generation of engineers while scaling systems that handle millions of events per second.
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Присоединяйтесь к Truecaller, чтобы проектировать архитектуру данных мирового масштаба и влиять на жизни 450 миллионов пользователей!
Описание вакансии
Join Truecaller – The place where innovation meets impact!
Truecaller's mission is to build trust in communication by making it safer, smarter, and more efficient. Born in Sweden, trusted by the world, and here’s why we stand out:
- We are trusted by over 450 million active users every month across 190+ countries
- We identify over 15 billion calls daily, helping users avoid spam and scams
- We are powered by a team of 450+ employees from 45+ nationalities
We always look for people who take initiative, own their work, and keep raising the bar. An entrepreneurial mindset matters here, especially when it turns bold ideas into real actions. We stay collaborative and focused, always searching for smarter paths forward. If you want to make an impact and grow with a team that inspires millions, you’ll fit right in.
The role:
You will play an important role in developing data pipelines, frameworks, and models to support the understanding of our users and better product decisions. You will help empower product teams with a complete self-serve analytics platform by working on scalable, robust solutions while collaborating with data engineers, data scientists, and data analysts across the company.
What you’ll do:
Drive Architectural Vision & Execution
- Design for Scale: Lead the architectural design and hands-on implementation of high-throughput, low-latency data pipelines using Spark and Kafka to process massive datasets.
- Build Core Infrastructure: Develop, optimize, and maintain robust data models in BigQuery and orchestrate complex, multi-layered data workflows using Apache Airflow.
- Operationalize AI/ML: Partner directly with Data Science teams to take machine learning models out of the lab and into production. Design the architecture that allows these models to serve real-time predictions and insights to downstream services.
Lead Complex, Cross-Functional Initiatives
- Navigate Ambiguity: Take ownership of highly ambiguous business problems, translating vague stakeholder requirements into concrete, actionable technical roadmaps.
- End-to-End Delivery: Autonomously drive complex, multi-squad projects from the initial whiteboard design phase through large-scale deployment and long-term maintenance.
- Cross-Business Alignment: Collaborate with Product Owners and technical leaders across different Business Units to ensure your data systems support the overall company strategy and maximize end-user value.
Elevate Engineering Standards & Team Performance
- Champion Quality: Actively lead large-scale refactoring efforts and continuously drive improvements in code quality, system reliability, and internal tooling.
- Proactive Problem Solving: Act as the vanguard for system health—spotting subtle, long-term performance bottlenecks early and architecting workable solutions before they impact the business.
- Mentorship & Coaching: Dedicate time to leveling up the broader engineering organization. Conduct rigorous architectural and peer code reviews, mentor senior and junior engineers, and promote a pervasive culture of technical excellence.
What you bring in:
Architecture & Tech Stack
- Core Engineering: Exceptional proficiency in programming in Python / Scala to build robust, highly optimized data systems.
- Distributed Systems: Extensive architectural experience with Apache Spark and Kafka (or equivalents like Flink, Kinesis, GCP Pub/Sub) to build high-throughput, low-latency pipelines handling AdTech-scale data (millions of events/sec).
- Data Warehousing & Orchestration: Expert in data modeling and query optimization in BigQuery (or Snowflake / Redshift). Proficient in orchestrating complex DAGs and workflows using Apache Airflow (or Dagster / Prefect).
- MLOps & AI Integration: Experience deploying machine learning models into production. Ability to engineer deployment architectures that translate AI models into scalable, low-latency APIs for downstream services.
Complex Execution
- End-to-End Ownership: Proven track record of leading complex, cross-squad projects from initial whiteboard design through large-scale deployment and maintenance.
- Problem Decomposition: Strong ability to break down highly ambiguous technical problems into clear, actionable development plans for multiple teams.
Engineering Leadership
- Engineering Standards: Drives the adoption of architectural best practices, robust testing methodologies, and high engineering standards across the organization.
- System Evolution: Leads large-scale codebase refactoring, system health improvements, and the evolution of internal tooling.
- Mentorship: Actively mentors senior and junior engineers, conducts rigorous architectural/code reviews, and guides the team's long-term technology choices.
It would be great if you also have:
- Transactional Databases: Hands-on experience with operational RDBMS (e.g., PostgreSQL, MySQL) and low-latency NoSQL data stores (e.g., Redis, Cassandra).
- Microservices Architecture: Experience building or integrating with microservice ecosystems, including API design (REST, gRPC) and container orchestration (Docker, Kubernetes).
- Infrastructure as Code (IaC): Proficiency in provisioning cloud data infrastructure using Terraform and configuring robust CI/CD pipeline
What we offer:
We support growth through learning resources, leadership programs, mentoring, and real hands-on work. People can move between teams and projects to build new skills and keep things interesting. We offer clear internal mobility and a transparent path for progression, with leaders who stay involved and provide guidance throughout the year. In addition, you will benefit from:
- A comprehensive compensation package: Learning and development allowance, voluntary provident fund (VPF) and/or national pension scheme (NPS) tax saving option provided, creche allowance
- Modern tools to do your best work: Choose your preferred computer and phone within our budget, so you can work comfortably and efficiently.
- A people-focused office culture: We value in-person collaboration and follow an office-first model, with some flexibility. Our offices offer a vibrant environment with opportunities to learn, connect, and recharge, from breakfast, lunch and quiet spaces to team activities such as movie nights, tech meetups, and cultural events. There's something for everyone.
- Truecaller’s “Lab Days” offer a space for imagination: 5 days each quarter, where everyone steps away from their normal tasks to explore new, bold ideas and build things they’ve always wanted to. It’s a space where curiosity leads the way, and prototypes take shape. Some concepts even make it into production, and a few have grown into real features used by millions today. Lab Days allow you to be creative, learn fast, and help shape Truecaller's future.
Come as you are:
Truecaller is committed to building a diverse and inclusive team. We believe that a wide range of backgrounds, perspectives, and experiences strengthens our products and our culture. No matter where you're from, what language you speak, or how you identify, we value what makes you unique and would love to get to know you.
Sounds like a great opportunity?
We will fill the position as soon as we find the right candidate, so please send your application as soon as possible. As part of the recruitment process, we will conduct a background check.
We only accept applications in English.
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Навыки
- Python
- Terraform
- Kubernetes
- PostgreSQL
- Microservices
- MLOps
- Redis
- Docker
- Apache Spark
- gRPC
- Apache Airflow
- Apache Kafka
- Google BigQuery
- Scala
Возможные вопросы на собеседовании
Для уровня Staff важно понимать, как кандидат проектирует системы, способные обрабатывать миллионы событий в секунду без потери данных.
Опишите ваш опыт проектирования высоконагруженных конвейеров данных на базе Kafka и Spark. С какими узкими местами вы сталкивались при масштабировании?
Вакансия требует опыта вывода ML-моделей в продакшн. Проверяется знание MLOps.
Как бы вы спроектировали архитектуру для обслуживания ML-моделей с низким временем задержки (low-latency) для 450 миллионов пользователей?
Staff-инженер должен уметь работать с неопределенностью и координировать работу нескольких команд.
Расскажите о случае, когда вам пришлось переводить расплывчатые требования стейкхолдеров в четкий технический план для нескольких команд.
Оценка навыков оптимизации затрат и производительности в облачных хранилищах.
Какие стратегии оптимизации запросов и хранения данных вы применяете в BigQuery для минимизации затрат при работе с петабайтными массивами?
Роль предполагает активное менторство и повышение инженерной культуры.
Как вы подходите к проведению архитектурных ревью и внедрению стандартов тестирования в большой инженерной организации?
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