Develops end-to-end data engineering solution projects. Contributes by estimating tasks and using an agile methodology. Ensures code quality and performance by adopting best engineering principles, following the defined data engineering lifecycle. Collaborates closely with multidisciplinary teams, contributing their expertise and learning from their peers to solve technical problems and actively participates in continuous improvement.Responsabilities: - Contributes to identify inputs and data origins, as well as the feasibility of requested requirements, identifying risk situations and communicating them to relevant participants during these sessions.
- Contributes to all phases of the data engineering lifecycle.
- Estimates the time it will take to code data solutions, considering the stages of the data engineering lifecycle, documentation, "go to production," and go-live.
- Performs ingestion or processing of structured and semi-structured data files, using relational and non-SQL databases
- Ensures the continuity of digital data solutions, insights, dashboards, etc.
- 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 data products throughout the data engineering lifecycle, project closure, and/or post-mortem.
Knowledge and Experience: - 2-4 years of experience as a Data Engineer
- In-depth understanding of core data engineering concepts and principles, including complex ETL (Extract, Transform, Load) processes, scalable data pipelines, and advanced data warehousing techniques.
- Advanced proficiency in Python, including writing efficient and optimized code, using advanced features like decorators, generators, and context managers.
- Extensive experience with Python libraries and frameworks commonly used in data engineering, such as Pandas, NumPy, PySpark, and Dask.
- Strong knowledge of both SQL and NoSQL databases, including advanced querying, indexing, and optimization techniques.
- Experience with database design, normalization, and performance tuning.
- Advanced understanding of data modeling concepts and techniques, including star schema, snowflake schema, and dimensional modeling.
- Experience with data modeling tools and best practices.
- Proficient in various data processing methods, including batch processing, stream processing, and real-time data processing.
- Extensive experience with data processing frameworks like Apache Spark, Apache Kafka, and Apache Flink.
- Advanced knowledge of file processing concepts, including handling large datasets and working with different file formats (e.g., CSV, JSON, Parquet, Avro).
- Strong understanding of data governance principles, including data quality management, data lineage, data security, and data privacy.
- Comprehensive understanding of the end-to-end data engineering lifecycle, including data ingestion, transformation, storage, and retrieval.
- Experience with CI/CD pipelines and automation for data engineering workflows.
- Advanced understanding of various data architectures, including data lakes, data warehouses, data marts, and data mesh.
- Experience designing and implementing scalable and robust data architectures.
- Proficient with version control systems (e.g., Git) and experience managing code repositories on platforms like GitHub or GitLab.
- Advanced understanding of data visualization tools (e.g., Tableau, Power BI) and reporting techniques.
- Ability to create insightful and impactful visualizations and dashboards.
- Strong understanding of cloud computing in AWS and GCP stacks.
- Basic experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
- Proven experience leading projects with Objectives and Key Results (OKRs), identifying risks, and delivering significant business value.
- Ability to mentor and guide junior data engineers.
- Strong ability to communicate project status transparently, including progress, challenges, and next steps.
- Effective collaboration with cross-functional teams, including data scientists, analysts, and business stakeholders
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.Si desea solicitar una adaptación, notifique a su Reclutador.
