Data Engineer / EF / São Jose dos Campos/SP

Work closely with data scientists, data engineers, and information management teams to support data needs, availability and usability. Participate in discussions to understand data requirements from Product Managers, Data Stewards, scientists, bioinformaticians and business stakeholders and R&D stakeholders Work as a full member of agile teams, participating in planning, daily stand-ups, reviews, retrospectives, and cross team- coordination (e.g., peer programming, retrospectives, architecture reviews) to align roadmaps, dependencies, and priorities Contribute code, configurations, and documentation to shared repositories Participate in code reviews, testing activities, and retrospectives as a learner and contributor Bachelor's (or Technology degree) in Computer/Software Engineering, Information Systems or related. Experience in data engineering / data pipelining. Postgraduate certificate in Data Engineering/Big Data/Data Architecture is a plus. Certifications: BigQuery/Azure Synapse/Redshift; Airflow/dbt; AWS/GCP/Azure data is a plus Insights in Machine Learning & ModelOps is a plus Availability to work on-site one (1) day per week in São Jose dos Campos/SP - GBS site Proficiency with cloud platforms (GCP, AWS, Azure) and their storage solutions (GCS, S3, Azure Blob Storage). Experience with serverless computing (Google Cloud Functions, AWS Lambda, Azure Functions). Strong knowledge of SQL and NoSQL databases, especially Google BigQuery. Familiarity with Big Data tools (Spark, Kafka, Flink, Hadoop). Proficient in Python for data manipulation and analysis. Familiarity with workflow/pipeline tools (Airflow, AWS Step Functions, KubeFlow). Experience with container orchestration tools (Docker) and version control (GitHub). Experience working in cross-functional or matrixed environments. Ability to independently translate business or scientific questions into analytical solutions Strong problem-solving and analytical thinking skills Effective communication with both technical and non-technical audience Knowledge in Data Governance practices is a plus Experience with data visualization tools (PowerBI, Spotfire) is a plus. Ability to manage multiple tasks and deliver results with limited supervision Engineering & MLOps fundamentals: version control, reproducibility; basic model lifecycle management including monitoring, maintenance, and continuous improvement of ML/AI solutions in production environments is desirable Machine Learning & Analytics: knowledge about supervised and unsupervised learning, practical experience implementation of ML approaches, Model evaluation & validation, Feature engineering, Understanding of ML limitation & data leakage is desirable

Global life science company with pharmaceuticals, consumer health, and crop science divisions. Headquartered in Leverkusen, Germany.

bayer.com