We are looking for software engineers with at least three years of experience and a passion for data.
We expect our data engineers to keep learning new technologies, architecture patterns, programming languages and machine learning algorithms. They will work with data scientists and are comfortable speaking their language. We are looking for professionals who are enthusiastic about innovation in software engineering and are not afraid to contribute to open-source projects and present at technical meetups.
As we help our clients to target, measure and improve mission-critical business metrics and generate demonstrable return on investment, our data engineers enjoy a high level of responsibility and immediate client interactions. In addition to technical excellence, our engineers are great communicators equipped with the presentation skills to operate at executive level.
Above all, our engineers are curious about the big picture and passionate about bringing data to life!
Engineer complete technical solutions to solve concrete business challenges in the areas of digital marketing, eCommerce, Business Intelligence and self-service analytics.
Collect functional and non-functional requirements, consider technical environments, business constraints and enterprise organizations.
Support our clients in executing their Big Data strategies by designing and building operational data platforms: ETL pipelines, data anonymization pipelines, data lakes, near real-time streaming data hubs, web services, training and scoring machine learning models.
Collaborate closely with partners, strategy consultants and data scientists in a flat and agile organization where personal initiative is highly valued.
Share data engineering knowledge by giving technical trainings.
Guide and mentor team members.
3 years of professional experience in software engineering
A broad practice in multiple software engineering fields:
Backend and/or frontend development in any programming language of your choice
Design of web services
Algorithms and complexity analysis
Linux system administration, development and production environments
Cloud, container and microservices infrastructures
Development workflow automation
A strong focus on data processing:
Databases, theory and practice
Distributed data processing
Real-time event processing
Concepts of functional programming
Data privacy and anonymization techniques
Enterprise data warehousing, business intelligence and ETL principles
Statistics and analytics