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.
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.
Solution:
Our engineers ramped up and started producing tangible results in less than 1 month. The Data Engineering department was created under our Tech Lead management. Its elements have been able to design and implement data management best practices frameworks that are already impacting general performance.
- Increased data integrity and availability.
- Data Pipelines have been standardized and their development time highly reduced.
- Technology stack has been reevaluated and optimized to reduce costs and increase performance.
- New requirements are already prioritized and planned accordingly, reducing Business uncertainty.
- With a dedicated DE team, responsibilities have been properly segregated, allowing better focus and performance from all the Engineering teams.
With our engineers, the Data Analytics team has doubled its size, reducing tech debt and development times.
- New charts and reports are being delivered on 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.
- Tech debt has been highly reduced.
Our Machine Learning engineers have rocked their world.
- Models have been optimized.
- 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’s projects are already in process of being deployed.
The QA department backs up our continuous delivery model, allowing our features to reach our final customers in a better, faster, more often and more secure manner than ever!
- Our QA Framework in half way designed and already being used by most of the development teams.
- Code coverage has reached a healthy level.
- As new technologies are being used, our engineers have come up with new solutions to properly check and validate code and functionalities.
Key Technologies:
AWS
Python Pandas
Python Scikit-learn
Java / Scala
Snowflake
MySQL
Sigma
Kubernetes
Docker
GLR
ReactJS
Blueshift
Fineract
Neo4J
Git
Data Feeds / Integrations:
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