8th Element
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Hyderabad · On-site · 10-13 Yrs · Oct 9
51 to 200 Employees · Monroe, US
Hiring: Engineering
What employees say
+ Good compensation they provided as per experience
− Don't have to say anything Cons
More reviews on the company profile.
Interview experience · AI/ML Intern
the interview was some what difficult it was of 3 rounds one techincal one manageral round and last hr round the technical round was okay but the mangeral round was difficult
Difficult difficulty · Hyderābād · 2 reports
More interview insightsJob description
Overview
We're a small team building AI-powered solutions for real enterprise problems — autonomous agents, intelligent workflows, and the data infrastructure that makes them work. We work on Microsoft Azure and Fabric, and we move fast.
Responsibilities
- Build and maintain data pipelines on Microsoft Fabric — ingesting data from enterprise systems into OneLake, keeping it clean, structured, and reliable.
- Support the AI and agent layer — work with the engineering team to make sure the right data reaches the right agent at the right time, in the right shape.
- Build dashboards and reports in Power BI that give stakeholders real visibility into what's happening operationally.
- Take ownership — pipelines break, data goes missing, things go wrong at the wrong time. You're the person who notices, fixes it, and tells the team what happened.
Qualifications
- Hands-on experience with Microsoft Fabric — you've worked on at least one real project involving Fabric pipelines, Lakehouse, or Power BI. You're not an expert but you're not starting from zero.
- Data literacy — you can look at a schema or a dataset and understand what the business is doing. You notice when something looks wrong without being told to check.
- Curiosity about the problem, not just the task — you ask what the data will be used for. You want to understand the system, not just your part of it.
- Ownership mentality — you don't wait to be told when something is broken. You don't close a ticket without making sure the problem is actually solved.
- Clear communication — you can explain a data issue to someone non-technical without losing them. You can write a clear summary of what you found and what you did about it.
- Willingness to learn fast — this project uses Azure AI Foundry, agent frameworks, RAG pipelines. You haven't necessarily touched these. You will. Fast.
- Engagement: 3-month project-based consulting role, with a possibility of extension based on client requirements and project needs.
- Good To Have
- Exposure to Azure services — Data Factory, Event Hub, Azure AI Search, Cosmos DB.
- Some Python or SQL beyond the basics — enough to debug a pipeline or transform data when a no-code tool doesn't cut it.
- Any exposure to AI projects — even at the edges. You've seen how an LLM or agent consumes data and have a sense of what "good data for AI" looks like.
- Skills: python,fabric data pipelines,microsoft fabric,lakehouse,power bi,data engineering,azure,sql
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Job application FAQ
Last updated 11 October 2026
- How do I apply for Data & AI Platform Associate (MS Fabric) at 8th Element?
- Open this job on Feedback AI and sign up free (or sign in). You can apply from the job detail page; match scores and skill gaps appear when your profile is complete. 8th Element may attach structured assessments to interview rounds after you apply.
- Does Feedback AI show a match score for this Data & AI Platform Associate (MS Fabric) role?
- Yes after you add a resume or skills. Match score ranks this role against your profile — skills met, gaps, level, and location — so you prioritise applications where fit is strongest.
- Is Data & AI Platform Associate (MS Fabric) remote or on-site in Hyderabad, Telangana?
- This listing is marked on_site with location Hyderabad, Telangana. Confirm details on the job description before applying.
- What skills matter for this Data & AI Platform Associate (MS Fabric) role at 8th Element?
- Key skills for this role include skills listed in the job description. Upload a resume to see which requirements you already meet and which are gaps.