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Data Engineer Assessment Tool
Summary
Full name of candidate
Data infrastructure & pipelines
As you will be leading the design of our data infrastructure, knowing what you know so far about what Birchgrove wants to achieve with its data what approach and tools would you recommend?
Remembering that I am not technical when it comes to data engineering are you able to present this in a way that helps explain your thought process? As we talk it through I'd be particularly keen to understand why you recommend this approach and why you have rejected other approaches. I'd also like to understand whether we should keep Fivetran and Snowflake in some way, or whether we should be looking to move away from these.
Q4: Can you walk me through your proposed architecture in plain terms? What does each layer do and why is it important?
Q5: What were your top priorities when designing this approach – speed, cost, scalability, something else?"
Q6: You’ve mentioned using [X tool]. Why did you choose that over other options?" (e.g., Airbyte vs. Fivetran, Snowflake vs. BigQuery)?"
Q7: How would this system perform if we doubled the number of data sources or neighbourhoods?"
Q8: "Are there risks or weaknesses in this approach we should be aware of?"
Q9: "Would we need ongoing engineering help to keep this running, or is it relatively low-maintenance?"
Q9: Imagine you left Birchgrove in 12 months. Would another person be able to understand and operate your setup?"
Q1: Three systems not support by Fivetran - how does an API work to set up data extraction?
Q2: No two systems have a consistent identifier, how do we overcome this to ensure we can link the data across platforms?
Q3: Data freshness - can we get to real time updates?
Comments & notes
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Analytics engineering & dashboards
Using any freely available data sample online, please could you create a visual dashboard using any tool of your choice.
For my own benefit, I'd been keen to better understand what the process entailed when you created the dashboard.
When it comes to Birchgrove, many of the people I have interviewed so far have mentioned PowerBI, Tableau and Looker as possible options. What are the advantages/disadvantages of each?
Q1: "Talk me through the steps you took to go from raw data to the dashboard I’m looking at."
Q2: PowerBI, Tableau, Looker or QuickSight?
Q2: "If we needed to scale dashboards across multiple neighbourhoods or teams, would that change your tool recommendation?"
Q2: "If I asked you to show me how a site is performing in under a minute – how would you design that dashboard?"
Q2: Is 3 months realistic timeframe for delivery?
Q3: KPIs we really need to track as part of our v1 leadership dashboard include:
Net operating income (NOI) per site
Occupancy % per site
Net move ins per site per month
New leads per site per month
Lead to visit conversion %
Visit to move in conversion %
Average length of stay
Achieved rental growth
Employee engagement (currently only changes once per year with an annual employee survey)
Employee turnover
Q2: "What techniques or methods do you use to make dashboards engaging and easy to interpret for non-technical users?"
Assesses UX thinking and storytelling skill.
Comments & notes
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AI & Predictive Modelling
Having done a little research myself I was particularly seduced by the promises from BlazeSQL. The idea of having an AI chatbot that sits on top of our database that would allow users to draw insights from a simple question is compelling.
Are there other/better ways to use AI with our data?
You know we are particularly keen to use predictive modelling when it comes to resident wellbeing, how would you see this working and what would we need to do to achieve this?
Q1: "What’s your impression of BlazeSQL or similar tools? Would they work well for us?"
Q2: "If we wanted to predict something like a change in a neighbour’s wellbeing – how would you approach that problem?"
Q3: "What data would you need to make that model possible?"
Q3: "How do you ensure AI recommendations are reliable, especially if someone’s health is involved?"
Q3: "Have you ever built or used a model where explainability was crucial? What did that look like?"
Q3: "How would you validate a predictive model before making it available to the wider team?
Q3: Sales& Marketing forecasting
Comments & notes
Soft skills
Innovation
Innovation
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Curiosity
Curiosity
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Problem solving
Problem solving
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Understanding of requirements
Understanding of requirements
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Comments & notes
Summary
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Notice period
Location
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