Domande di colloquio presso BlockLabs

Written Assessment - Head of Data Engineering Overview This written assessment is part of our interview process. It is not a test and there are no right or wrong answers. We are interested in how you think, how you handle ambiguity, and how you make trade offs in environments where context shifts and direction evolves. Time: 90 minutes Format: Written, open book Style: Bullet points are welcome You may ask clarifying questions in writing if you wish. You should assume you will not receive answers. Part 1: Establishing Control in a Moving System Context You join Block Labs as Head of Data Engineering. The company operates in a fast moving environment with evolving priorities, incomplete information, and frequent context switching. Within your first 60 days, three things happen at the same time: A strategic partner changes direction. A key upstream dataset you expected to build on will not be available for at least six months, and may not arrive at all. The CEO asks a broad question: “Can we get to reliable, near real time operational reporting this quarter, and reduce manual spreadsheet work?ˮ No clear success criteria are defined yet. You discover that core operational data is fragmented across multiple systems, inconsistently defined, poorly documented, and in some cases partially owned or controlled by external vendors. Several teams rely on manual exports, spreadsheets, and ad hoc scripts to compensate. You have no additional headcount approved yet, and the longer term data strategy is still forming. Prompt Describe how you would approach the next 30 days. You may include: How you would frame the situation and define the problem in a way leadership can align on What you would prioritise vs defer (and why) 1 Written Assessment  Head of Data Engineering How you would decide where to standardise first: definitions, pipelines, tooling, access, governance, or reliability How you would communicate uncertainty and risk (including data quality risk) How you would balance quick wins with foundations that prevent repeated fire drills What you would explicitly choose not to do yet Try to be specific about outputs you would aim to deliver by day 30 (even if imperfect). Part 2: Designing a Practical Data Platform Roadmap Context You inherit a landscape that looks roughly like this: Multiple data sources: product events, operational tooling, finance systems, vendor exports A mix of batch and manual processes, with unclear ownership No single source of truth for key metrics (different teams compute them differently) Stakeholders who want dashboards and answers, but definitions are unsettled Compliance and audit expectations are increasing Leadership wants progress quickly, but also wants to avoid building something that collapses under growth. Prompt Propose a 90 day roadmap that gets the business to a noticeably better place without over engineering. Please cover: Your guiding principles (for example: reliability first, semantic consistency, minimum viable governance) Your recommended target architecture at a high level (no vendor names needed, but you can include them if helpful) How you would approach: Ingestion (sources, change data capture vs batch, vendor feeds) Modelling (raw, staging, curated, semantic layer, metric ownership) Data quality (tests, monitoring, contracts, incident handling) Access and security (least privilege, auditability, PII handling) Documentation and discoverability (catalogue, ownership, definitions) What you would deliver at the end of: 30 days 60 days 90 days What you would explicitly de scope in the first 90 days, even if stakeholders ask 2 Written Assessment  Head of Data Engineering Part 3: Building Trust Through Delivery Context Some teams are frustrated. They feel the “data functionˮ historically says no, moves slowly, or produces outputs that do not match how the business actually works. At the same time, you see real risks: Metrics are inconsistent Data is used for decisions without confidence levels Pipelines fail silently Vendor controlled data has unclear SLAs Ad hoc access patterns raise security and compliance concerns Prompt Explain how you would rebuild confidence across the company. Please include: How you would align stakeholders on metric definitions without endless debate How you would handle competing priorities from the CEO, Ops, Finance, Product, and Engineering What you would measure to prove progress (beyond “more dashboardsˮ) How you would set expectations about reliability, latency, and correctness What your escalation and incident approach would look like for data issues How you would avoid becoming the team that everyone queues behind Part 4: Building a Small but Durable Team Context After initial alignment, you receive approval to hire up to five people for a Data Engineering team over the next 12 months. Constraints: The roadmap is fluid and likely to change Data is seen as both an opportunity and a risk You expect high context switching and evolving scope You are accountable for both delivery and sustainability Prompt Describe your approach to building this team. Please cover: The five roles or profiles you would prioritise (job titles are not required) 3 Written Assessment  Head of Data Engineering What you would deliberately avoid hiring at this stage How you would structure responsibilities so the team does not become a bottleneck How you think about platform work vs analytics enablement vs stakeholder embedded work Your approach to on call, ownership, and operational load as the team scales Part 5: Navigating a Strategic Pivot Context Six months in, leadership decides that: The company will optimise for operational excellence and predictable reporting. Near real time analytics and advanced experimentation are now lower priority. The focus is reliable financial and operational truth, governance, and cost control. As a result: Some planned initiatives are no longer relevant Some work in progress may be stopped or reframed The team is aware that direction has changed Prompt Explain how you would: Reset direction with your team and stakeholders Decide what to stop, what to keep, and what to reframe Protect morale and focus during the change Handle sunk cost and prior commitments without losing credibility Ensure the platform still supports future needs, without betting on them Part 6: Leadership and Self Awareness Please answer the following briefly and honestly:  What kind of data engineering problem do you personally tend to over optimise?  What kind of decision do you tend to delay longer than you should?  What type of person do you find hardest to work with, and why?  What would make you feel that staying in this role after 18 months is no longer the right choice for you? Final Notes This exercise will be used as a starting point for a deeper conversation in the next interview. We are not expecting perfect answers, only thoughtful ones.