Data Engineer
At ZONTAL, we pride ourselves in providing structured, interoperable data to our customers to help make informed business decisions and reduce manual data processing overhead. We’re seeking an experienced pipeline-centric data engineer to make this a reality for our ever-growing portfolio.
This role requires taking on many diverse and evolving responsibilities but focuses on building our Python ETL processes and writing superb DAGs. Beyond technical prowess, the data engineer will need soft skills for clearly communicating highly complex data trends to organizational leaders. We’re looking for someone willing to jump right in and help our customers get the most from their data.
Work with data to solve business problems, building and maintaining the infrastructure to answer questions and improve processes
Help streamline our data science workflows—through thoughtful automation, reusable pipeline patterns, and AI-assisted development workflows—adding value to our product offerings and building out the customer lifecycle and retention models
Work closely with the data science and business intelligence teams to develop data models and pipelines for research, reporting, and machine learning
Use agile software development processes to make iterative improvements to our back-end systems
Model front-end and back-end data sources to help draw a more comprehensive picture of user flows throughout the system and to enable powerful data analysis
Build data pipelines that clean, transform, and aggregate data from disparate sources
Collaborate with data scientists, analysts, and software engineers to understand data needs and deliver high-quality datasets.
Ensure data quality, integrity, and security through validation, monitoring, and governance practices.
Automate data workflows and improve data processing efficiency.
Monitor and troubleshoot data pipeline issues and performance bottlenecks.
Three or more years of experience with Python, and data visualization/exploration tools
Literacy in using AI-assisted development workflows to accelerate development, paired with strong judgment around correctness, security, and data privacy
Ability to work on a dynamic, research-oriented team that has concurrent projects
Knowledge of GDPR, and best practices for data privacy and protection.
Familiarity with Git, Docker, and CI/CD pipelines for data workflows.
Proficiency in working with Linux-based systems for deploying, monitoring, and maintaining data infrastructure.
Experience working with RESTful APIs for data ingestion, transformation, and integration with third-party systems.
Ability to create clear, comprehensive technical documentation for data pipelines, architecture, and processes.
Bachelor’s degree (or equivalent) in computer science, information technology, computational biology, engineering, or related discipline
Experience with real-time data processing
Background in biological informatics or clinical data analysis is a plus
Familiarity with deploying and managing containerized data applications in Kubernetes environments for scalability and reliability.
Experience with Elasticsearch for indexing and querying data is a plus
Familiarity with Terraform and AWS CDK for provisioning and managing cloud infrastructure.