data engineer for biomedical research

AstraZeneca develops pharmaceutical products and conducts early drug discovery research. Its computational infrastructure supports biomedical research and the company’s drug discovery pipeline.

• Design, develop, and maintain applications, including APIs, backend services, and user interfaces supporting scientific workflows;
• Write clean, efficient, well-tested, and well-documented code following modern software engineering best practices;
• Build and integrate databases with efficient data access patterns and API layers;
• Maintain and enhance existing applications while developing new features and capabilities;
• Build and maintain data processing pipelines that ingest, transform, and integrate scientific data across the organization;
• Implement ETL workflows, data validation, and quality checks for reliable data delivery;
• Work with various data formats, sources, and storage systems supporting research data needs;
• Optimize data pipelines for performance, reliability, and scalability;
• Collaborate with scientists to understand computational and data requirements;
• Work with senior engineers and the technical lead on design approaches and implementation strategies;
• Participate in code reviews, contributing to code quality and knowledge sharing;
• Document technical decisions, system architecture, and data workflows;
• Troubleshoot and resolve issues across applications and data pipelines independently;
• Participate in CI/CD pipeline development and deployment processes;
• Collaborate with IT teams on infrastructure, security protocols, and production deployments;
• Support incident response and monitoring of production systems.

• Bachelor’s degree with 5+ years or Master’s degree with 3+ years of professional software development experience;
• Demonstrated delivery of production applications or data systems;
• Strong proficiency in Python for application development and data processing;
• Experience with FastAPI, Flask, Django, pandas, NumPy, and scikit-learn;
• Hands-on experience building and maintaining data pipelines, ETL workflows, and data processing systems at scale;
• Experience with SQL and NoSQL databases, including schema design, query optimization, and data access layers;
• Experience with RESTful APIs, backend services, and application integration with data systems;
• Familiarity with AWS, Azure, or GCP, Docker, and Git;
• Strong problem-solving and debugging skills across application and data infrastructure;
• Good communication skills and ability to collaborate with technical and scientific stakeholders;
• Nice to have: experience in scientific computing, bioinformatics, or pharmaceutical/biotech environments with biomedical data; workflow orchestration tools such as Airflow, Prefect, Nextflow, or Snakemake; data platforms such as Databricks or Snowflake; data lakes, data warehouses, and large-scale data storage architectures; TypeScript/JavaScript and React, Vue, or Angular; microservices architecture, API design patterns, and DevOps practices; production AI/ML model integration or deployment; Go, Rust, or C++; security best practices and compliance requirements in regulated environments.

• 3 Days in office and 2 remote per week.