استخدام DataOps Support Engineer(Remote)

What you’ll bring/do

Did you read Our Core Values? No, you didn’t. Go read it now, and then come back to talk more. That short list of values is the most important measure for us.

We’ll wait here for you. Thanks.

(waiting patiently…)

Ah, good. You’re back. Were you nodding emphatically while reading Our Core Values, as if you write those? Fantastic! Let’s talk about specific qualifications then:

🕵🏻 Relentless Root-cause Analysis

Do you like puzzles and solving problems? Great! Your primary responsibility is clarifying and resolving production issues of the data platform built by our team. You will be gathering clues by asking "Why?" meticulously so that you can identify and fix the root cause.

🗣️ Effective Communications

You’ll be at the first layer of receiving reported issues through various contact channels (Jira, Teams/Slack, email, and meetings). You’ll keep the project stakeholders in the loop with accurate and correct info, ensuring they're confident that issues are handled with care and skill. Your good command of speaking and writing in English is crucial for this role.

🤹🏼‍♀️ Ticket/Incident Management

Staying on top of your tickets is the name of the game. You must identify, simplify, and triage the reported tickets/incidents to the right channel. Your reactions can vary from disqualification of a ticket to hotfixing it yourself or escalating it to the data engineers in the team. We're talking crystal-clear updates on status and progress so stakeholders can get the lowdown at a glance – saving you from a barrage of update calls. You always stay alert, focused, calm, and collected, even when dealing with P1 incidents.

📄 Multiply Yourself by Sharing

Your love for clear and concise documentation will shine here too. You'll write and record yourself a lot! This includes (but is not limited to) updating README.md in a Git repository, using meaningful commit messages, keeping changelog wiki pages up to date, elaborating context on Jira issues, drafting release logs and one-pagers, and publishing a postmortem after a major incident.

💻 Codebase and Business Logic Modification

You'll often need to adjust the codebase or modify business logic in response to stakeholder or client requests. Comfort with coding and the ability to align business logic with user needs are crucial. This involves technical skill and a clear understanding of how these changes affect the system's functionality and goals.

What you’ll be working with

🐍 Python

Given that our platform and pipelines are built with Python, you'll need a solid understanding and be hands-on with Python to navigate and contribute to our codebase with confidence.

🪣 SQL

Get ready to roll up your sleeves and work with SQL and especially Athena to sift through data like a pro. If you’re a pro with SQL on a Database engine, you’ll have a good time in this role!

📦 Git

Got a knack for Git? Great, Git isn't just a tool, it's a way of communication. Branching, pulling, merging, rebasing, cherry-picking – these should be your second nature to communicate with other engineers.

♾️ CI/CD

Every deployment dances to the rhythm of CI/CD. You'll play a crucial role in this seamless choreography of continuous integration and deployment.

We’d love you to have (extra/nice to have)

👨🏼‍🍳 These are not the topics that we’ll interview about but having knowledge and experience about them will 10x your chance of being selected by DataChef.

🚀 Spark

Knowing your way around PySpark**,** even just the basics, is another feather in your cap. It'll help you troubleshoot and contribute to the project more effectively. We’ll also teach you about this.

👁️ Grafana and Cloudwatch

A little know-how in Grafana and Cloudwatch would also be a real asset. While we don't expect you to have extensive experience with these tools, a beginner-level understanding of Monitoring, observability, and finding your way through logs and metrics, will help you stay one step ahead in spotting and addressing issues.

🪢 Kafka

If you have a basic understanding of Kafka, that's a big plus! If you’re able to check if producers and consumers are doing their thing correctly by looking at key metrics like offset and consumer log, that is already very nice.