Median Rp6,5 jt/bulan · rentang umum Rp5,0 jt – Rp7,3 jt
Berdasarkan 259 lowongan QA & Testing se-Indonesia (semua level).
Lihat data gaji selengkapnya →for pricing and market intelligence use cases. You will own the acquisition layer—from understanding external systems and extracting data to delivering reliable landing data for downstream processing.
Build and maintain reliable web/app crawlers, scrapers, and external API integrations across e-commerce, marketplace, retail, and other external sources.
Analyze browser, mobile application, API, and network traffic to understand how data is delivered and design the appropriate acquisition approach and runtime, from direct HTTP/API extraction to browser automation.
Build acquisition systems that remain reliable as external websites, applications, APIs, and data contracts evolve.
Handle real-world source complexity such as sessions, tokens, pagination, dynamic content, rate limits, retries, concurrency, and partial failures.
Build resilient and maintainable acquisition systems with appropriate testing, idempotency, deduplication, retries, safe reruns, data-quality validation, monitoring, and alerting.
Operate acquisition workloads using appropriate workflow orchestration, compute, data storage/warehousing, and cloud infrastructure.
Continuously improve reliability, scalability, maintainability, and total cost of ownership, including evaluating build-vs-buy approaches for external data.
Explore new technologies and AI-assisted development or automation to improve how external-data pipelines are built, operated, and maintained.
Collaborate with Pricing, Data Warehouse Engineering, Data Analysts, and other stakeholders to turn external-data needs into sustainable platform capabilities.
Hands-on experience with browser/network debugging and the ability to investigate how websites or applications communicate with their backend services.
Experience with both HTTP-based extraction and browser automation, using tools such as Requests/HTTPX, Selenium, Playwright, Scrapy, or equivalent.
Understanding of production reliability patterns such as retry, timeout, rate limiting, concurrency, idempotency, and failure recovery.
Solid understanding of SQL, data storage/warehousing, schema evolution, workflow orchestration, containerized workloads, and cloud infrastructure fundamentals.
Data lowongan bersumber dari kalibrr. Tombol “Lamar” mengarahkan Anda ke halaman aslinya.