After around 3 years of working with great talents, I had the opportunity to take responsibility for the entire tech department at Talentwunder.
With these new responsibilities, my tasks changed as follows:

• Hands-on tasks reduced to 30%.
• Architecting the entire Talentwunder software landscape.
• Supporting AI, Data Engineering, and QA teams.
• Strategic, organizational, and budget planning.

Challenges

Technical

Previously, I served as the serving leader for the SaaS (Web Development) Team. With my new responsibilities, I was excited to also serve the AI and Data Engineering teams. Having already had numerous touchpoints with these teams during my time leading the SaaS Team, our collaboration came naturally.

I successfully transferred many advantages that the SaaS team had been using for a longer period to the AI and Data Engineering teams. This included the CI/CD process, designing systems to reduce the engineering workload for the AI team so they could focus on building great models, and using the same framework for the Data Engineering team that the SaaS team had been using.

Organizational

At the start of my CTO tenure, there was no clear structure for the AI and Data Engineering teams, mainly due to the limited number of engineers:

• AI: 1 Senior Data Scientist and 1 Working Student.
• Data Engineering: 2 Data Engineers.

One significant change I implemented was creating a dedicated QA position, previously handled by our Product Owner.

Achievements

Shortly before I assumed this position, there was turbulence in the Tech Team due to the previous CTO leaving the company within a short period. This led to a couple of engineers also deciding to leave the company.
I successfully built highly effective engineering teams with a high employee retention rate. Additionally, the teams are consisted of international members based in and outside Germany. I managed to create a healthy working culture, which was challenging to achieve due to language and cultural differences.

Technical

Together with the Product Owner, we implemented measures to increase our Customer Retention Rate, which was previously low due to various complex reasons. We shortened release cycles for the AI team, addressing issues caused by excessive tasks and complex communication.
By increasing team members in Data Engineering, we offloaded engineering tasks from the AI team, enhancing the throughput for our Data Scientists.
And Data Engineering teams, previously focused solely on scraping, now handle more interesting tasks, including DevOps responsibilities.

Organizational

I succeeded in building high-performing teams, consisting of:

• SaaS: Expanded from 1 engineer to 6 engineers.
• AI: Expanded from 1 engineer to 4 engineers.
• Data Engineer: Expanded from 2 engineers to 3 engineers.
• QA: Established a new team with 1 engineer.