• Responsible for developing, testing, deploying, monitoring, and maintaining processes that provide data for analytics. • Participate in defining and designing solutions for medium-impact projects, aligning stakeholder requirements and collaborating with the digital product team. • Collaborate with digital product teams, influencing architectural decisions and data integration strategies. • Implement quality checks, housekeeping policies, and access controls, following data engineering best practices. • Develop and maintain data flows that combine established solutions and new challenges, assessing opportunities for optimization. • Evaluate, define, and implement data models that ensure efficient storage, access, and processing. • Actively participate in squad ceremonies established by the squad leadership, contributing to the effectiveness of team activities.

• Own the EDA platform end-to-end • Optimize for scale • Build for researchers • Enable cross-functional users • Manage infrastructure • Provide support

• As a Senior Data Engineer, you will be the ideal person to support the Engineering area; your primary responsibility will be the scalable maintenance and creation of solutions for the data infrastructure. • Inspect, clean, transform, and model information, which is fundamental for decision-making. • Support the design of data processing components on Layer7 infrastructure that meet the specific needs of the company and its clients and suppliers. • Design, implement, and maintain data pipelines to integrate different sources. • Actively collaborate with data analysts, engineers, and cross-functional teams to understand and translate data requirements and propose tailored solutions. • Build metrics and implement observability tools to track and measure data pipeline performance for early incident detection. • Support the data governance process, maintaining data privacy criteria and the security regulations established by Layer7. • Document architectures, data flows, and process automation.

• You'll work across the full lifecycle of data engineering - from designing elegant solutions to deploying them in production • Building and maintaining data pipelines of varying complexity - from straightforward transformations to advanced probabilistic algorithms that drive our data science capabilities • Designing thoughtful solutions for new challenges, with emphasis on clean architecture and maintainability • Writing automated tests because we believe quality is built in, not bolted on • Fixing bugs and improving existing systems - our codebase evolves constantly • Exploring data when problems require investigation and analysis • Contributing to backend services and internal tools that support our data infrastructure • Deploying your work - you own it from concept to production