Objective of the role:
Leads and contributes to end-to-end data engineering projects, ensuring the efficiency and scalability of data architectures. Provides expert estimation of development tasks using Agile methodology. Their experience in execution patterns is essential to ensure code quality by adopting best practices following the defined data engineering lifecycle. Contributes to optimizing performance and technological resources involved in data solutions. Collaborates with other technical leaders, sharing knowledge, and proactively guiding less experienced peers in problem-solving.
Main Responsabilities:
- Actively contributes to all phases of the data engineering lifecycle with Agile methodology.
- Estimates timelines for the data solutions under their responsibility, considering the stages of the data engineering lifecycle, "go to production," and go-live.
- Contributes to the definition of data architecture based on their experience.
- Contributes to the design and maintenance of data models that enable and adapt to business needs or requirements, using industry techniques and best practices that provide value to the business.
- Analyzes and proposes technical solutions for data storage using best practices, standards, and data governance guidelines, data privacy strategies, security, and compliance.
- Performs data ingestion and/or processing of structured, semi-structured, and unstructured files (including videos and images), as well as batch, microbatch, near real-time, and/or real-time data processing; hosting information in containers, storage, or datalakes; enabling valuable information that drives decision-making in the business unit where they are assigned, through Insights, reporting, Deep dive, Metrics - PMM Performance Management Model - and/or Artificial Intelligence).
- Ensures the continuity of digital data solutions, insights, dashboards, etc., to build and consolidate a Data-Driven culture.
- Collaborates and contributes to the development of data monitoring processes and quality metrics to ensure that the data used by the business is reliable, intact, and complete.
- Documents processes or diagrams related to data architecture / processes / technical memory, to ensure continuity and efficient execution in a productive environment.
- Generates code versions in the repository, artifacts, and components of data solutions and/or products throughout the data engineering lifecycle, project closure, and/or post-mortem.
Required Knowledge and Experience:
- 4 - 6 years of experience in a similar role.
- Advanced technical mastery 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 (non-SQL), 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.
Digital FEMSA está comprometida con un lugar de trabajo diverso e inclusivo.
Somos un empleador que ofrece igualdad de oportunidades y no discrimina por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad u otra condición legalmente protegida.
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