Avanade Data Engineer interview typically runs 4 rounds: HR, technical, manager, director. It usually takes a few weeks and is highly client-project oriented.
$101K
Avg. Base Comp
$145K
Avg. Total Comp
4
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Avanade is looking for more than a strong data engineer who knows the Microsoft stack — they want someone who can operate comfortably in a consulting rhythm. The technical conversation leans heavily toward Synapse, Data Factory, and especially Databricks, but it’s not framed like a pure platform quiz. We’ve seen a mix of tool depth, light programming, and logic/math questions, which suggests they’re checking whether you can reason through implementation choices rather than just recite features.
A recurring theme is that the hiring team cares a lot about how you work with clients and managers. One candidate described the manager conversation as distinctly more consultative, with emphasis on handling stakeholder relationships and working in a managed environment. That matters because the process seems to reward people who can translate technical work into delivery on client projects, not just build pipelines in isolation. We also see a strong signal around project-based flexibility: the bench, assignment changes, and even travel to client sites came up as important realities that weren’t always made explicit early.
The non-obvious make-or-break factor here is fit with the commercial model. Multiple details point to a role where title, compensation, and responsibility are tightly linked to whether you manage people or sit on client work, and one candidate ultimately declined because the offer was rigid and the package felt low for the scope. In other words, Avanade seems to screen for Azure capability, but it closes on whether you’re comfortable with a consulting structure that is client-driven, assignment-driven, and less predictable than an in-house data engineering role.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Avanade process.
Sono stata contattata su LinkedIn e il processo è stato abbastanza lineare: prima un colloquio con HR, poi una parte tecnica e infine un confronto con il manager. La parte HR è stata quella che mi ha convinta meno, mentre sia il tecnico sia il manager mi sono sembrati più interessanti e in linea con il ruolo. Nel complesso avevo la sensazione di essere perfettamente allineata con quello che cercavano, soprattutto sul lato Azure e data engineering.
Nel colloquio tecnico mi hanno chiesto soprattutto Synapse, Data Factory e, più di tutto, Databricks. Oltre alle domande sui tool, c’erano anche alcuni quesiti di logica e matematica e qualcuna di programmazione, quindi non era solo una chiacchierata sullo stack ma nemmeno un coding interview pesante. Con il manager invece il tono è diventato più consulenziale: hanno esplorato come mi muovo in un contesto gestionale e di relazione con il cliente, più che la parte puramente tecnica. Un altro manager e il director sono entrati nel processo più avanti, e lì ho percepito un taglio molto orientato al lavoro su commessa. Una cosa che non mi era stata chiarita subito è il tema della bench, cioè i periodi tra un progetto e l’altro, e il fatto che il lavoro sia molto legato alle assegnazioni dei clienti, con anche spostamenti presso le sedi esterne.
Alla fine ho rifiutato l’offerta. La RAL non era negoziabile e, se non hai persone da gestire, non vieni inquadrata come Consultant, quindi la proposta economica risultava piuttosto bassa. L’azienda mi è sembrata interessante per le competenze che si possono acquisire su progetti Azure, ma tra retribuzione, stabilità e trasferte non era il fit giusto per me.
Prep tip from this candidate
Ripassa bene Synapse, Data Factory e soprattutto Databricks, perché sono stati i temi più centrali del tecnico. Preparati anche a domande di logica/matematica e a spiegare il tuo approccio in ottica consulenziale, non solo tecnica.
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| Question | |
|---|---|
| Why Do You Want to Work With Us | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Employee Salaries | |
| Closest SAT Scores | |
| Merge Sorted Lists | |
| Largest Salary by Department | |
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| Prime to N | |
| SELECTive Wine Connoisseur | |
| Rolling Bank Transactions | |
| Find the Missing Number | |
| Over-Budget Projects | |
| Manager Team Sizes | |
| Hurdles In Data Projects | |
| Retailer Data Warehouse | |
| Size of Joins | |
| First Touch Attribution | |
| Project Budget Error | |
| Top 3 Users | |
| The Brackets Problem | |
| Maximum Profit | |
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| Google Maps Improvement | |
| Top 5 Turnover Risk | |
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Synthesized from candidate reports. Individual experiences may vary.
The process starts after being contacted on LinkedIn and begins with an HR conversation. This stage focuses on background, motivation, and basic fit for the consulting environment, though the candidate noted it felt less compelling than the later stages.
A technical round covers Azure data engineering topics, especially Synapse, Data Factory, and Databricks. The interview also includes some logic and math questions plus a few programming questions, but it is not described as a heavy coding interview.
The manager conversation is more consultative and explores how the candidate works in a client-facing, managerial context. The discussion goes beyond pure technical skills and looks at communication, delivery on projects, and fit for a consulting role.
Later in the process, another manager and a director join for a more senior discussion. This round is oriented toward project-based work, including expectations around client assignments, bench time, and occasional travel to client sites.