About the Role

We are seeking multiple Data Engineers to join a integral data and analytics programme within a highly regulated pharmaceutical and life sciences environment. You will play a key role in designing, building, and maintaining scalable data pipelines that support critical business initiatives across clinical development, manufacturing, process development, and active pharmaceutical ingredient data domains.

Working within a modern cloud data ecosystem, you will help deliver trusted, high-quality data products while ensuring compliance with GxP regulations and industry standards.

Key Responsibilities

- Design, develop, and orchestrate automated data ingestion and transformation pipelines across multiple enterprise systems, including SAP, LIMS, and MES.
- Build scalable ELT/ETL solutions using SQL and Python within a modern cloud data platform.
- Develop modular and reusable transformation logic following medallion/layered architecture principles.
- Implement workflow orchestration and scheduling for batch and streaming data pipelines.
- Drive CI/CD adoption, automated testing, version control, and deployment best practices.
- Embed data quality, lineage, monitoring, and observability controls to ensure data integrity and reliability.
- Optimize data processing performance, scalability, and platform costs.
- Ensure all solutions comply with ALCOA+ principles, 21 CFR Part 11, EMA,



and other regulated industry requirements.
- Support the delivery of enterprise data products and analytics capabilities that enable informed business decisions across global operations.

Required Technical Skills

- Apache Airflow (workflow orchestration)
- Snowflake (cloud data platform)
- dbt (data transformation and modelling)
- Talend (data integration)
- Amazon S3 (data lake storage)
- GitLab (source control and CI/CD)
- Collibra (data governance and lineage)

Essential Experience

- 3-10 years of Data Engineering experience.
- Strong SQL and Python development skills.
- Experience designing and implementing ELT/ETL pipelines.
- Knowledge of dimensional modelling and medallion architecture.
- Hands-on experience with CI/CD pipelines and Git-based development workflows.
- Understanding of data quality frameworks, lineage, observability, and monitoring.
- Experience with performance tuning and cloud cost optimisation.
- Ability to work within complex enterprise environments and collaborate with cross-functional teams.

Desired Domain Experience

Experience within one or more of the following areas would be highly advantageous:

- Clinical trial data
- Pharmaceutical manufacturing
- Process development
- Active ingredient data management
- Knowledge graph solutions
- Substance and product data domains
- GxP-regulated environments

📌 Data Engineer (México)
🏢 Templeton u0026 Partners - Innovative u0026 Inclusive Hiring Solutions
📍 México