Full Stack Data Engineer - Data Analytics and Ai Zapopan, Jalisco, Mexico

Location Zapopan, Jalisco, Mexico
Job ID R-
Date posted 16/06/2025
Location: Hybrid (3 days on-site in Guadalajara, MX; 2 days remote) *:
About AstraZeneca :
AstraZeneca is a global, science-led biopharmaceutical company focused on the discovery, development and commercialisation of life-changing medicines. Our Enterprise AI team delivers the AI app store, reusable AI models and ready-to-use ML platforms that power everything from early-stage drug discovery to patient-safety systems.
You will be joining the Data Analytics and AI (DA&AI) organization within Operations IT, responsible for delivering state-of-the-art Data Analytics and AI solutions for Operations capability areas like Pharmaceutical Technology Development, Manufacturing & Global Engineering, Quality Control, Sustainability, Supply Chain, Logistics and Global External Sourcing and Procurement. Here our work has a direct impact on patients - transforming our ability to develop life-changing medicines. We empower the business to perform at its peak and lead a new way of working, combining cutting-edge science with leading digital technology platforms and data.
As a Full stack data engineer, you will Collaborate with a diverse team of UI/UX designers, architects, full-stack engineers, data engineers, and DevOps engineers to develop high-impact, scalable, and innovative products that deliver actionable insights and support our business goals.
Key Responsibilities :
- Develop, Maintain, and Optimize the scalable data pipelines for data integration, transformation, and analysis, ensuring high performance and reliability.
- Demonstrate proficiency in ETL/ELT processes, including writing, testing, and maintaining high-quality code for data ingestion and transformation.
- Improve the efficiency and performance of data pipeline and workflows, utilizing advanced data engineering techniques and best practices.
- Develop and maintain data models that represent data structure and relationships, ensuring alignment with business requirements and enhancing data usability.
- Develop APIs and microservices for seamless data integration across platforms and collaborate with software engineers to integrate front-end and back-end components with data platform.
- Optimize and tune databases and queries for maximum performance and reliability and maintain existing data pipelines to improve performance and quality.
- Mentor other developers on best practices, conduct peer programming, code reviews, and help evolve systems architecture to consistently improve development efficiency.
- Ensure compliance with data security and privacy regulations and implement data validation and cleansing techniques to maintain consistency.
- Stay updated with emerging technologies, best practices in data engineering and software development, and contribute to all phases of the software development lifecycle (SDLC) processes.
- Work closely with data scientists, analysts, stakeholders, and product managers to understand requirements, deliver high-quality data solutions, and support alignment of data sources and specifications.
- Perform unit testing, system integration testing, regression testing, and assist with user acceptance testing to ensure data solutions meet quality standards. Work with the QA Team to develop testing protocols and identify and correct challenges.
- Maintain clear documentation for Knowledge Base Articles (KBAs), data models, pipeline documentation, and deployment release notes.
- Utilize and manage cloud-based services (e.g., AWS) for data storage and processing, and implement and manage CI/CD pipelines, version control, and deployment processes.
- Create and maintain data visualizations and dashboards to provide actionable insights.
Essential Skills :
- Minimum 7+ years of experience in developing and delivering software engineering and data engineering solutions.
- Extensive experience with ELT/ETL tools such as SnapLogic, FiveTran, or similar.
- Deep expertise in Snowflake, DBT (Data Build Tool), and similar data warehousing technologies.
- Proficient in designing and optimizing data models and transformations for large-scale data systems.
- Strong knowledge of data pipeline principles, including dimensional modelling, schema design, and data integration patterns.
- Familiarity with Data Mesh and Data Product concepts, including experience in delivering and managing data products.
- Strong data orchestration skills to effectively manage and streamline data workflows and processes.
- Proficiency in data visualization technologies, with experience in advanced use of tools such as Power BI or similar.
- Solid understanding of DevOps practices, including CI/CD pipelines, version control systems like GitHub. Ability to implement and maintain automated deployment and integration processes.
- Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
- Automated testing frameworks (Uni