Data Analytics Engineer

Vacancy

As a Data Analytics Engineer at Zenz Technologies, you work together with clients to give them optimal insight in their data. You do this partly by means of extensive implementation projects, and partly by delivering support once the software has been implemented. Clients are often airlines and train companies, and you work closely together to fully understand their business needs and wishes.

As Zenz aims to maintain long term relationships with their clients, it is essential to ensure the client receives high quality support. You do this by building a strong relationship with the clients’ stakeholder(s) as well as the application users. You can assist clients where needed because you are easily approachable and have extensive knowledge of the software. You understand the coding behind the software and are able to translate the clients wishes into clean code. Besides this, you also host business visits or digital meetings to show the users the software’s features.

You are the first point of contact for the clients under your responsibility. Therefore, it is important to have business, customer and programming skills.

If you are interested in programming and data, you like to contribute to an industry-leading analytics platform with creative ideas, and do not mind to work with clients from all over the world, then this is the job for you.

€3.450 - €3.850 gross p/m (starters salary)
Profit-sharing scheme
40 hour workweek
28 vacation days
Opportunities to follow external trainings and courses
Phone, laptop and flexible working (from home) possible
Pension plan

	Mon	Tue	Wed	Thur	Fri
08:00	Content	Content	Content

Working
week

Wednesday

"On a less enjoyable day, a customer changed his mind late in the project. This means I have to re-do some work. Luckily, I anticipated this by designing a flexible logic."

	Mon	Tue	Wed 🤔	Thu 😊	Fri
08:00
08:00
09:00
09:00	Reading and answering	Optimizing data ingestion pipelines (C++) Improving performance on large datasets		Providing support by debugging a complex data issue	Continue working on adding a data source to customer app using c++
10:00	Team meeting		Zenz Matters	Providing support by debugging a complex data issue
10:00	Team meeting		Zenz Matters	Worksession with colleague
11:00	Helping a colleague who's stuck	Asking for help to someone of my team	Testing and debugging my project
11:00	Contacting colleague	Call with client	Contacting colleague	Call with client
12:00	Lunch	Lunch	Lunch	Lunch	Lunch
12:00	Lunch	Lunch	Lunch	Lunch	Lunch
13:00	Debugging customer data inconsistencies Investigating RM anomalies	Working session with client	Implementing RM calculation logic (C++)	Going live with client	Progress meeting with client
13:00				Pingpong	Progress meeting with client
14:00				Progress meeting with client	Internal meeting with project team and partner
14:00	Pingpong	Meeting with colleague		Progress meeting with client	Internal meeting with project team and partner
15:00	Giving demo to users		Providing support via a screenshare call to help a user build a custom dashboard		Performance tuning on large datasets (C++)
15:00	Giving demo to users	Working on adding a data source to customer app using c++		Working on adding a data source to customer app using c++
16:00	Providing support	Working on adding a data source to customer app using c++	Meeting with client	Working on adding a data source to customer app using c++
16:00	Providing support		Meeting with client
17:00

Project
example

Integrating Ancillary Data for an Airline ✈️

Adding an extra data source to an analytics platform may sound easy. However, every project has its own challenges.
Below, you’ll find a description of a real project that we worked on last year.

An airline wanted to integrate ancillary revenue into the Zenz platform. Ancillaries include all purchases made
besides the flight ticket itself, such as seat reservations or cancellation fees. The client provided sample data,
which was used to configure the required dimensions and KPIs. After configuration, the client validated both the
usability and the correctness of the data in the application.

Ancillaries can exist on two different levels:

Booking level (e.g. cancellation fees)
Flight level (e.g. pre-reserved seats)

To make the data more valuable for analysis, the ancillary data needed to be merged with the main booking data so
analysts could view ancillary information alongside all other booking details.
Merging big data volumes while keeping the platform fast and flexible is
typically difficult in traditional dashboarding tools, but Zenz provides scalable solutions for this use case.

Challenges & adding business expertise

For flight-level ancillaries, the data was matched directly to the corresponding flight. For booking-level ancillaries,
flight information is technically not defined. However, our users are used to analysing data linked to flight groups.
Therefore, we explored options to link it to flights anyway.

We considered simply linking it to the first flight we find per booking, but a more meaningful solution was implemented
by distributing the ancillary revenue across all flights we found for that booking. To make this data more valuable for
analysts, the revenue was pro‑rated based on flight distance.

Learning along the way

At Zenz, we believe it is impossible to foresee all challenges at the start of a project. Therefore, we work with quick
iterations and use learnings along the way to work towards a final solution. In this project, we faced interesting
challenges related to flight dates and memory usage.

More specifically, booking-level ancillaries do not contain a flight date. Zenz stores data based on departure month,
but we do not know in which departure month we can find a match in the booking data. Loading all possible months would
lead to excessive memory usage and longer processing times.

This was solved by selectively scanning booking data across multiple departure months and storing only records matching
the relevant PNRs in preparation for the merge. After further investigation, we found that only a small percentage of the
ancillary source data consisted of booking-level ancillaries. As a result, the solution was optimised by:

Merging flight-level ancillaries directly with their corresponding departure month
Handling booking-level ancillaries separately, significantly reducing memory usage and processing time

This approach resulted in a scalable, fast and flexible integration that delivered enriched, analysis-ready ancillary
data to the client. Once validated, the full historical data was loaded into the tool, allowing the airline’s ancillary
team to start using Zenz for their analysis.

Ask a question directly

Raymond Kat

First hand experience

A train company wants to analyze the load factor of its trains, to maximize the number of tickets sold, and prevent wasted empty seats. How do you help them?

Together with the client, you discuss the business need and technical impact. On average a train can be quite empty on a route, but if there is one segment on the route that is already sold-out, no-one can buy a ticket from the start to the end anymore. Together, you decide that it is more interesting to look at the ‘maximum load factor’, to report per train the load factor of the segment with the highest load factor. With the internal Zenz team you discuss the technical impact, because this customised solution should of course still perform very fast.

Depending on your experience and personal preference, you will program the feature or outsource this to a Software Engineer colleague. You keep the client updated on the progress, and will demonstrate the feature once it is unrolled.