СШАSr. Software Engineer, Data Engineering (X)
2 дн. назад
СШАSenior
distributed computingdata pipelinesbatch processingrealtime processingdata engineering
Senior data engineer role focusing on building and operating distributed data platforms and pipelines to serve various internal and external stakeholders.
Обязанности
- Design, build, and operate production-grade realtime and batch pipelines that ingest, process, validate, and deliver data powering user-behavior insights and product decisions
- Create shared datasets, fact tables, and internal data products that let other teams analyze, debug, and improve product performance
- Prototype and build tooling that automates and accelerates internal data workflows — backfills, dashboards, report generation, and self-serve access to data
- Own data correctness end to end: validate with output invariants, denominator reconciliation, and independent recomputation, and lead root-cause investigations when key metrics move unexpectedly
- Move fluidly across query engines and frameworks (e.g., BigQuery, Trino, Clickhouse for analytics; Flink, Kafka, Spark/Scalding for streaming and batch), choosing the right tool and adapting quickly to new infrastructure and environments
- Partner across product and business teams to surface where data gaps exist and prioritize the highest-impact opportunities for new data acquisition and improvement
- Iterate quickly on feedback, shipping the smallest useful increment with a strong bias toward efficient, accurate, and reliable solutions
Требования
- 5+ years of professional software engineering experience
- Experience with Python, Rust, Scala, Go or Java, and data pipeline toolings and distributed systems
Будет плюсом
- Software engineering experience in data engineering or distributed systems
- Knowledge of realtime and batch data processing tools such as Spark/Kafka/Flink/SQL and various storage systems in RMDBs/NoSQL
- Experience solving large scale problems and comfortable doing incremental quality work while building brand new systems to enable future quality improvements
- Proven records of interpreting product requirements into engineering implementation plans, and effectively communicating with different groups (AI, product, marketing/sales and engineering)
Условия
- $180,000 - $330,000 USD
- Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.
Другое
- SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
- As a Data Engineer in X product engineering team, you will play a key role in providing comprehensive data solutions that own or serve a wide range of stakeholders, including our end-users and internal teams such as Product engineering, Algorithm, Legal, Finance and Sales. Our work utilizes AI, distributed computing and hybrid storage technologies, but extends beyond purely data-centric solutions, encompassing non-data-related challenges, ultimately maximizing the potential of data for the benefit of our diverse user base.
- You'll help build and operate a distributed data platform that powers hundreds of realtime and batch pipelines processing billions of events per day. The team runs like an internal startup — we own problems end to end, from raw event streams to the datasets, tooling, and metrics that product, growth, safety, and business teams depend on daily.