Questions to ask in interviews

Interview is a two-ways street: it enables the future employer to know more about your likelihood to fit into the team; but it is also for you the best opportunity to know more about your future management-style, team and colleagues. It is a rare chance to develop a feeling for the company before you accept (or reject) the job offer. It might either confirm or quash your initial beliefs. Last but not the least, it is also a way for you to give a very nice first impression. Your aim is to show that you are already projecting yourself into the job, striving to be a technical asset and social enabler for those you gonna work with.

Note: if you are looking for red-flags, you will always find some. Sometimes, ignoring what you think might be off is a good strategy. For me, receiving the job offer very shortly after the second interviews (less than a few days) always felt a bit weird but more often led to very pleasant experiences. Of course, there is no way to tell wether or not you have dodged the bullet until you have waited long enough for the trigger to be pulled. The evaluation period is also a way for you to test the water. In the rare occasions where should the trial period be a bummer, you can always resume it. Turn the associated perks to your own benefit and do use it!

Straight to the point, hereafter the questions. Feel free to take from it:

  1. What your typical day is looking like? What are the key milestones of your days and weeks?

  2. What are the main technical and managerial challenges you are currently facing with? What are the solutions your are walking forward?

  3. How the team stays in tune with the current and emerging technologies?

  4. What the onboarding journey will look like? What are the learning paths or processes in place? What is your mentoring process?

  5. Before taking any final decision, would it be possible for me to meet the whole team and the manager?

  6. When could I start?

  7. What can I do to surpass your expectations and be a positive element of your team and organization?

  8. What degree of initiatives one can have within the team? How are you enabling the teams to be self-directed and proactivity?

  9. What is your technical stack? What are the provided working devices? What kind of access rights do people have on their equipments?

  10. What technical debts do you have? How are you coping with it?

  11. How do you bring the team together? What are the biggest concerns shared across the team at the moment?

  12. Who are your main stakeholders?

  13. What is the vision the team is striving for? How the team is stirring toward those goals?

  14. How are you making sure you are keeping track with the road map?

  15. How are you coping with errors and mistakes to occur?

  16. Are there any career milestones and evolution pathways already in place? What the perspectives would look like?

  17. How the scopes, milestones, timelines and deliveries of a project are estimated?

  18. How are you disambiguating ambiguous problem statements to get to the root of problems, incoming requests and situations?

  19. What amount of details should I provide to the manager for him to stay in the loop without drawing him in unnecessary information? What is the satisfactory update frequency one should adopt?

  20. Where do you draw the line, finding the good balance between action and delivery but without over-compromising on quality?

  21. How is your code, legacy and processes documented? What is your estimated coverage?

  22. What are the standards and best practices you have in place to guarantee good codebase quality? How to ensure the reliability of your data pipelines?

  23. Regarding Git and Gitlab, what are your main CI pipelines jobs consist of?

  24. What is your home-office policy? What actions are in place to stimulate the “working together” sentiment?

  25. Beside my mother tongue, I do speak english at a very proficient level. I however ensure to speak german – which I consistently learn since two years with the objective of being perfectly fluent by 2025 – on a minimum daily base. I can so far hold causal conversations. I intend to adopt the mean of communication the team is the most comfortable with. Should it be german, what would you expect from me to ease my integration within the team, quickly close any cultural gaps that might be and promote effective cooperations?

  26. Which data stage are you? E.g. Monolithic on-premises systems or moved already on the Cloud. Reverse ETL in place? What are your observability and DataOps (DevOps and FinOps) strategies?

  27. Is there an explicit agreement (SLA/SLO) between the upstream data source teams and the data engineering team?

  28. Who are your upstream and downstream stakeholders?

  29. How is the data architects/data engineering tandem working? How involved are each parties in the decision-making process?

  30. Is the workloads internal-facing (upright stream from source systems to analytics and ML teams) or external-facing (feedback-loop from the application to the data-pipeline)?

Note: those questions have a purpose. On top of providing useful information for you to make your choice, they are matching the inquiries any Senior Data Engineer might have. Proving at the same time that you have already owned your way in the Senior team. And if you have already those concerns in mind, congratulations, you are a Senior Data Engineer! 🥳

See also: Are you a Senior Data Engineer?

What is a Data Engineer

What is a Data Engineer in a nutshell

A Data Engineer is like a gas or oil pipeline operator. He must:

  • oversee the full Cloud Data Warehouse;
  • make data available 24/7 on the platform;
  • move data from A to B;
  • ingest, update, retire or transform data coming from upstream data sources;
  • turn data into by-products and monitor the overall flow.

On top of ingesting, transforming, serving and storing, those tasks mandates strong proficiency in Security, Data Management, Data|FinOps, Data Architecture, Orchestration & Software Engineering.

The main objective is to serve data to the analysts/BI/data science teams + back to the software teams (reverse ETL).

It enables the company to make data-driven decisions (an example of data driven decisions is given at the end).

One big aspect though: a Data Engineer brings the oil to the different teams but is not responsible for consuming it. He is not responsible for turning data into meaningful charts or actionable decisions. We mostly do not bother much about the business-logic behind. Our role is to save data into a place (being the unique source of truth) where it can be consumed by the teams who need it.

Note: A lot of organizations or applicants tend to have a very poor understanding of what is really meant behind the term Data Engineering. They rather see the position as a patchwork of different roles and responsibilities. The profession is indeed quite new – as you can see on Google Trend the interest only exploded in late 2019. It will still needs some time before people (me included) have a full grasp over it.

Instead, Data Engineer is a technical job that requires you to be proficient in writing code (mainly Python and Java). Therefore, you need to have strong Software Engineering skills. Developers (more than Data Scientists or Data Analysts) are in turn highly valued. That is why I prefer to call it Data Software Engineer to remove any ambiguity.

The different missions

  • Build, Orchestrate and Monitor ELT pipelines (using Airflow & Google Cloud Composer).
  • Manage data infrastructures and services in the Cloud (e.g. tables, views, datasets, projects, storage, access rights, network).
  • Ingest sources from external databases (using Python, Docker, Kubernetes & Change Data Capture tools)
  • Develop REST API clients (facebook, snapchat, jira, rocketchat, gitlab, external providers)
  • Publish open-source projects e.g. on github.com/e-breuninger such as Python libraries, Bots or Google Chrome Extensions (ok, this one is rather specific to my job)
  • Lead workshops and interviews (e.g. BigQuery Introduction, Code Standardization & Best Practices)

Keep in mind: Data Engineers are Data Paddlers, not Data Keepers.

If the source data is corrupted, correcting or improving data quality is out of scope. You can see us as an incorruptible blind-folded carrier, moving the baggage assigned to us from A to B, without looking into it throughout the journey. You don’t want us to start opening the bags and fold your different shirts as it should be.

See Should Data Engineers work closely with the business logic?

Tools used by a Data Engineer

At least, those I am currently working with on a daily base:

  • Airflow to manage your ETL pipelines.
  • Google Cloud Platform as Cloud Provider.
  • Terraform as Infrastructure as Code solution.
  • Python, Bash, Docker, Kubernetes to build feeds and snapshots readers.
  • SQL as Data Manipulation Language (DML) to query data.
  • Git as versioning tool.

The main challenges in the job

Based on my own experience, my biggest challenges at the moment are:

  • Improve existing pipelines reliability (pushing for more tests, ISO-standardization, data validation & monitoring)
  • Get rid of the technical debt (moving toward 100% automation, documentation and infrastructure as code coverage)
  • Keep up with the upcoming technologies (learning new skills, going more in-deep vertically and horizontally)
  • Enforce the software development best practices and standardization principles (via multitude hours of workshops and conferences)
  • Strengthen the international part, actively connecting the teams (via department tours and promoting the use of english as primarily source of communication)

I believe them to be representative in this industry.

Get started as Data Engineer

This will need an article on its own. However, you can get started with the immediate following take-aways:

Technical books

  • Fundamentals of Data Engineering, Reis & Housley, O’REILLY, 2022
  • Google BigQuery: The Definitive Guide, Lakshmanan & Tigani, O’REILLY, 2019
  • Terraform: Up & Running, 3rd Edition, Yevgeniy Brikman, O’REILLY, 2022
  • Learning SQL, 3rd Edition, Alan Beaulieu, O’REILLY, 2020
  • Docker: Up & Running, 3rd Edition, Kane & Matthias, O’REILLY, 2023
  • The Kubernetes Book, Nigel Poulton, Edition 2022

Online courses

  • The Git & Github Bootcamp, Colt Steele on Udemy
  • Apache Airflow: The Hands-On Guide, Marc Lamberti on Udemy
  • Terraform Tutorials, HashiCorp Learn

An example of Data Driven Decisions

Imagine you are the CEO of a bicycle rental company. You have multiple stations across Manhattan. You have the following 3 want-to-know questions:

  • You want to know which stations perform the best and are in high demand so you can anticipate any disruptions ahead, having more technicians standing-by in the area, increase the fleet and anticipate future expansion.
  • On the other hand, you want to retire poorly performing stations, adjusting your implantation to make it fit the market needs more accurately.
  • You want to monitoring the overall usage so you know what are the off-peaks and rush hours, average journey length or most appreciated commute options. You can then adapt the offer accordingly, e.g. offering discounts at specific times of the day/week/month to boost customer acquisition or match better your customers’ needs.

It is part of the Data Engineering journey to consume data coming from the different sources (e.g. bikes stations, bicycles, Open-Weather API, Google Map API etc.) so the marketing and business intelligence teams can solely focus on answering your questions without getting their hands dirty, deep-diving into the data ingestion part.

To conclude, as it is often the case in history, recent jobs have many similarities with sectors that have existed long before them. For instance, the data engineering field and the energy sector share closed similarities (you have to move an expandable from A to B and distribute it to consumers). They simply inherit the lingo. Ideas remain the same but are now applied to different “objects”. Data has replaced oil but the paradigm keeps working.

However, good luck getting your car to run with it! 🏎️💨