Design and maintain data architectures, contributing to the scalability and reliability of data lakes, warehouses, and real-time streaming platforms.
Develop monitoring processes and data quality metrics to ensure the reliability, integrity, and completeness of data used for decision-making.
Actively contribute to all phases of the data engineering lifecycle, adhering to Agile methodologies and ensuring timely delivery of high-quality solutions.
Mentor and guide junior and mid-level engineers, fostering a collaborative and inclusive team environment.
Maintain and document data architectures, processes, and technical workflows to ensure continuity and knowledge sharing.
Lead the integration of APIs and external data sources, optimizing data flows and ensuring compatibility with existing architectures.
Manage version control and repository practices to maintain clean, reliable, and maintainable codebases.
Promote an autonomous work culture by encouraging self-management, accountability, and proactive problem-solving among team members.
Serve as a Spin Culture Ambassador to foster and maintain a positive, inclusive, and dynamic work environment that aligns with the company's values and culture.
Required Knowledge and Experience
Minimum 5–6 years of experience in data engineering or related fields, with proven expertise in designing and implementing scalable data solutions.
Advanced technical expertise in software development life cycle (SDLC) methodologies.
Advanced understanding of design patterns.
Able to work autonomously and provide support to less qualified professionals.
Advanced experience in the use of Python, Java, and data-related frameworks.
Advanced knowledge of dimensional data modeling and advanced techniques in this field, including structured, semi-structured, and unstructured data storage (NoSQL), as well as ETL, ELT construction.
Advanced knowledge in file processing: CSV, JSON, Parquet, YAML, images, and videos.
Advanced knowledge in data processing: batch, microbatch, near real-time, and real-time.
Advanced understanding and experience to contribute and implement data privacy strategies.
Advanced understanding and experience in implementing and maintaining robust data governance strategies.
Advanced knowledge in Data Engineering Lifecycle (also known as Data Processing Lifecycle).
Advanced knowledge in processing different types of internal and external data sources with API management, for subsequent processing, storage, and analysis.
Advanced knowledge in understanding Architecture: Business, Solutions, Data, etc.
Advanced mastery in version control: Git, GitHub, GitLab.
Advanced in business vision (business domain), to sensitively identify how data contributes to the business.
Advanced mastery and evidence with projects using the Google Cloud Platform (GCP) Stack for Data.
Advanced mastery and evidence with projects using the Amazon Web Services (AWS) Stack for Data.
Advanced experience in developing and optimizing SQL and related queries.
Able to effectively and exceptionally socialize the design of the data solution without supervision.
Advanced experience in Project Management with Agile methodology (Scrum or Kanban).
Experience in collaborating on projects to achieve OKRs, warning of risks, and providing business value.
Advanced competence in transparently communicating project status.
En Spin estamos comprometidos con construir un lugar de trabajo diverso e inclusivo.
Creemos en la igualdad de oportunidades y promovemos un entorno libre de discriminación por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad o cualquier otra condición legalmente protegida.
