Solution: Dedicated Team
The challenge:
- As a Fintech with a strong Machine Learning backbone, the company was struggling with building the required teams to fulfill its growth necessities.
- Tech debt and projected features were falling behind. Existing personnel could barely maintain the current platform.
- Expected market growth was surpassed by far, increasing the need to improve current services and increase the features portfolio.
- Data Engineering responsibilities were distributed amongst existing Analytics, Data Science and Engineering teams.
The Engagement:
- Our hiring process covered all the client needs and more. Fanning from practical tests, live technical interviews, cultural assessment and English language verifications, our candidates proved to be over the expected level.
- Provided a team of professionals covering all the technical areas involved in the client’s platform development and maintenance.
- Our Engineers were selected thinking of providing extra value for the client. Focused on finding tech/business gaps inside the company that not even the client were aware of.
- As part of the company, these elements would merge with the client’s culture and mindset, providing a transparent interaction with the rest of the departments.
- Communication skills are strongly enforced, as part of our customer satisfaction vision.
Solution:
Data Engineering:
Data integrity and availability have been increased in 25%.
Data pipelines have been standardized and their development time has been reduced in 17%.
The technology stack has been reevaluated and optimized to reduce costs 15% and increase performance 19.5%.
New requirements are being prioritized and planned accordingly, reducing business uncertainty.
Data Analytics:
The data analytics team has doubled its size, reducing tech debt and development times.
New charts and reports are being delivered on a timely manner.
Best practices have been implemented, cleaning existing reports data gaps and preventing them in newly created ones.
New projects have been designed and are in queue to be implemented.
Machine Learning:
Models have been optimized in 70%.
Implementation pipelines have been designed and implemented, allowing for better performance.
Tech debt has been reduced to its minimal level in the client’s history.
New market projects are already in process of being deployed.
QA:
The QA framework is halfway designed and is already being used by most of the development teams.
Code coverage has reached a healthy level (80%).
Tech Lead / Manager
- Agile Coach certified. Vast experience implementing Agile processes and mentoring teams into them.
- Deep knowledge of Data Engineering, Analytics and Machine Learning platform concepts and technologies.
- Over 15 years of development experience in several technologies.
- Experience with mentorship and team growth best practices.
- Excellent communication skills, able to translate business requirements into clear technical points.
Data Engineers
- Deep Python/Pandas understanding.
- Experts in Database interconnectivity and ETL processes.
- Usage of several Data tools and frameworks.
- Cloud oriented development experience.
- Proactive and highly motivated.
Machine Learning Engineers
- Python Scikit-learn experts with strong statistical knowledge.
- Experience creating, implementing and optimizing AI models.
- Technical Versatility, able to perform on different tech environments and produce excellent results.
Data Analysts
- Great experience with Data management and Business Intelligence.
- Knowledge of top-notch technologies.
- Incredible adaptability, quickly learning new technologies.
- Strong SQL knowledge, able to optimize the most challenging queries.
QA Engineers/Architect
- Business Focused.
- Experience on fully automated environments’ best practices and technologies.
- Able to design and implement a QA framework that covered all the areas involved in the platform.
- Focused on spreading knowledge around, enabling all the engineers to build code based on QA requirements.
