ClickHouse is a popular, Open-Source OLAP Database. We are ClickHouse Inc, the company behind the database aiming to build the best in class real time data warehouse.
Things my team works on:
Scale To Zero
Vertical and Horizontal Scaling
Make Before Break Scaling (see some of my KubeCon talks - [0] and [1])
ClickHouse Kubernetes Operator
Recommendation Systems (Time Series Analysis, Forecasting algorithms, Data Engineering)
ClickHouse Internals (every once in a while)
What we are looking for:
Cloud / Kubernetes operator engineers to build the AutoScaling infrastructure. We also maintain our own ClickHouse-based Data Infrastructure. Bonus points if you've worked on autoscaling or other data-driven forecasting challenges in the past.
Can work in an early stage startup environment. Proactive and hands on.
Senior/Staff+ Engineer. You must be independent and self-organized.
Good at Go, Kubernetes (Understanding how to manage stateful services in a multi-cloud environment)
We have a Python service in our Recommendation pipeline, so some ML/Data Science knowledge would be good.
SQL / ClickHouse SQL (goes without saying)
You'll need to be able to do On-Call - debugging production infra or db issues
If you can do low-level database programming (C++, ClickHouse) that's added bonus.
If you want to work on operating production critical databases in the cloud on k8s + write data-driven algorithms for autoscaling, consider applying!
Send me an email: manish.gill [at] clickhouse [dot] com
ClickHouse | Senior Software Engineer - Cloud Infrastructure / Kubernetes | Remote (US / EU preferred, APAC can be considered if you're outstanding at remote work + communication)
ClickHouse is a popular, Open-Source OLAP Database. We are ClickHouse Inc, the company behind the database aiming to build the best in class real time data warehouse.
Things my team works on:
Scale To Zero
Vertical and Horizontal Scaling
Make Before Break Scaling (see some of my KubeCon talks - [0] and [1])
ClickHouse Kubernetes Operator
Recommendation Systems (Time Series Analysis, Forecasting algorithms, Data Engineering)
ClickHouse Internals (every once in a while)
What we are looking for:
Cloud / Kubernetes operator engineers to build the AutoScaling infrastructure. We also maintain our own ClickHouse-based Data Infrastructure. Bonus points if you've worked on autoscaling or other data-driven forecasting challenges in the past.
Can work in an early stage startup environment. Proactive and hands on.
Senior/Staff+ Engineer. You must be independent and self-organized.
Good at Go, Kubernetes (Understanding how to manage stateful services in a multi-cloud environment)
We have a Python service in our Recommendation pipeline, so some ML/Data Science knowledge would be good.
SQL / ClickHouse SQL (goes without saying)
You'll need to be able to do On-Call - debugging production infra or db issues
If you can do low-level database programming (C++, ClickHouse) that's added bonus.
If you want to work on operating production critical databases in the cloud on k8s + write data-driven algorithms for autoscaling, consider applying!
Send me an email: manish.gill [at] clickhouse [dot] com
I'm not sure if I should be applying but before I do, I'm wondering what kind of novel or interesting data driven algorithms you refer to here. Are you referring to like adaptive query planners based on runtime statistics? Real-time detection of query latency spikes? Automated creation of secondary indexes based on usage patterns? Statistical smarts to decide what to cache and when? These would be sort of runofthemill for any modern database product, can you clarify if you are looking for any of these?
Asking in good faith out of genuine curiosity: I kind of associate ClickHouse with Yandex. What's the present-day relationship and legal setup, and how does it jive with Western sanctions against Russia?
They tried running minecraft, but I wonder if a similar (or better) cloning is possible for a mission critical workload - like a database consuming a huge amount of memory. Neon uses QEMU to achieve this for example: https://neon.tech/docs/reference/glossary#live-migration but is that the only way?
If people are still coming up with you for questions that the docs answer, then the docs are not discoverable. Nobody is going to sit through hours of docs to find that one small thing they are interested in knowing the answer to _right now_.
As I said, people doing this aren't interested in looking for an answer. They're interested in making their problem someone else's problem. An AI agent doesn't solve this.
Insofar as "the problem" is sifting through pages and pages of documentation to find the relevant information for an answer, I think that current-day LLMs are actually quite capable at this.
Before needing an LLM for it, they might want to ensure their doc system is using a search system that actually works. There's too many doc-focused templates / apps that have the worst search possible.
I think I qualify for EB-1C, but does one need to be present and already living in the US to apply for it, or can that happen from the current country of residence?
Or is the general path: L1 -> Move -> Apply for EB-1C once migrated.
Most people go the EB1C green card route after they are in the U.S. in L-1A status but that's not required; the entire green card process can be done while the applicant is outside the U.S. and not yet employed by the U.S. company.
Is it recommended or not? I've heard anecdotally that going through the Consular services to obtain EB1C is not recommended - because you can get denied easily without any protections you might get from being in the US.
You don't have protections in the US per se. If you are on a visa, your visa will remain valid and continue on its own terms. Your GC application is orthogonal.
ClickHouse is a popular, Open-Source OLAP Database. We are ClickHouse Inc, the company behind the database aiming to build the best in class real time data warehouse.
I am hiring for a Senior Software Engineer to work on our AutoScaling recommendation pipeline, used to do vertical and horizontal scaling database clusters for ClickHouse Cloud.
We are looking for people who have production experience in building data products - think time-series forecasting, anomaly detection using heuristics, stats or ML models. The ability to dig deep into metrics quality, statistics explainable algorithms for Auto-Scaling are crucial. Things like simulations, back-testing etc are useful to know.
If you happen to be have Data Engineering or Data Infrastructure experience as well, that's cherry on top.
You'll be working across the stack - the Infrastructure, Kubernetes Operators and innards, Data Pipelines, but most critically, the Recommendation Pipeline. If you're into Databases, we also work closely with the Core database team to support the Auto-Scaling needs.
Stack: Go for the Cloud Systems, Python for the Recommendation Services.
ClickHouse is a popular, Open-Source OLAP Database. We are ClickHouse Inc, the company behind the database aiming to build the best in class real time data warehouse.
Things my team works on:
What we are looking for:Cloud / Kubernetes operator engineers to build the AutoScaling infrastructure. We also maintain our own ClickHouse-based Data Infrastructure. Bonus points if you've worked on autoscaling or other data-driven forecasting challenges in the past.
If you want to work on operating production critical databases in the cloud on k8s + write data-driven algorithms for autoscaling, consider applying! Send me an email: manish.gill [at] clickhouse [dot] com[0] https://www.youtube.com/watch?v=AFoMsLMZKik
[1] https://www.youtube.com/watch?v=ufY_JFPpzRI
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