Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Роль четко определена с акцентом на архитектуру платформы данных и современные технологии, плюс конкурентоспособный диапазон зарплат, что делает это хорошей возможностью на рынке технологий.
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Высокая зарплатаСовременный стек технологийПродуктовая компанияНет спонсорства визыВысокие ожидания
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Senior Staff Engineer, Data (Apache Spark): Designing and evolving FloQast's core lakehouse data platform across ingestion, governance, storage, processing, and access with an accent on Apache Spark, Iceberg, Kafka, and cloud-native data systems. Focus on defining distributed compute architecture, scaling reliable batch and streaming pipelines, and establishing platform standards for data quality, security, observability, and cost efficiency.
Location: San Jose, California; hybrid workplace
Company
hirify.global develops a SaaS platform with a core data foundation supporting product, analytics, and business operations.
What you will do
Lead the architecture of scalable data pipelines, storage solutions, access layers, and lakehouse systems.
Own event streaming, batch and streaming ETL, data cataloging, and foundational data platform components.
Define standards for data quality, lineage, privacy, access control, and regulatory compliance.
Make compute architecture decisions for a platform supporting more than 30,000 tenants.
Collaborate with Product, Analytics, Infrastructure, and Security teams to align platform capabilities with organizational goals.
Mentor engineers, establish coding and architecture standards, and evaluate technologies that improve performance, observability, developer experience, and cost efficiency.
Requirements
10+ years of software engineering experience with deep expertise in data infrastructure, distributed systems, or backend platform engineering.
Production experience designing data platforms at scale, including hundreds of terabytes of data and thousands of jobs per day.
Deep Apache Spark experience, including partitioning, shuffle behavior, memory management, Catalyst optimization, PySpark or Scala Spark, Structured Streaming, and Spark UI debugging.
Experience with Apache Iceberg, Kafka, Airflow, Snowflake, dbt, and cloud-native technologies such as AWS, GCP, or Azure.
Experience building APIs and services for data access, metadata management, and platform observability.
Knowledge of GDPR, SOC2, HIPAA, enterprise security practices, and effective cross-functional communication.
Nice to have
Experience in a startup or high-growth SaaS environment.
Exposure to AI/ML data pipelines or real-time analytics platforms.
Culture & Benefits
Values authenticity, integrity, growth, collaboration, and customer focus.
Hybrid engineering environment with opportunities to influence platform strategy and technical standards.
Equal-opportunity employment practices and an inclusive professional workplace.
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