Senior Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in Brazil.

This role offers the opportunity to design and scale a next-generation data platform supporting a rapidly growing financial technology ecosystem.
You will build highly reliable data infrastructure capable of processing hundreds of millions of events daily across multiple data sources.
The position combines distributed systems expertise, cloud engineering, and data architecture to solve complex technical challenges.
You will work closely with engineering, analytics, and operational teams to enable better decision-making through accessible, trusted data.
As part of a fully distributed team, you will have significant ownership in shaping infrastructure, tooling, and engineering practices.
This opportunity is ideal for an experienced data engineer passionate about open-source technologies, scalability, and building impactful platforms.

Accountabilities:

The Senior Data Engineer will be responsible for designing, developing, and maintaining scalable data infrastructure that supports business intelligence, analytics, AI-driven experiences, and external data integrations. The role requires strong technical ownership, reliability-focused engineering, and collaboration across multiple teams.

• Design, build, and evolve core data platform infrastructure, including distributed query engines, orchestration systems, data warehouses, and data cataloging solutions.
• Own and improve lakehouse infrastructure as code, managing deployments through Terraform, Ansible, Kubernetes, and related cloud-native technologies.
• Develop and maintain low-latency streaming and change data capture (CDC) pipelines, along with batch ingestion workflows using modern data architecture patterns.
• Build scalable data solutions based on open table formats and object storage technologies to support analytics and AI use cases.
• Improve BI capabilities by enabling self-service access to reliable and performant data for internal teams and automated systems.
• Implement platform reliability practices, including monitoring, alerting, incident response processes, runbooks, maintenance procedures, and service-level objectives.
• Partner with DevOps, Analytics Engineering, and other stakeholders to identify infrastructure gaps and deliver solutions for evolving data needs.
• Contribute to data experimentation, governance, cataloging, lineage, and platform optimization initiatives.
• Participate in technical decision-making and help establish best practices for scalable, secure, and maintainable data systems.

Requirements:

The ideal candidate has extensive experience building production-scale data platforms and strong knowledge of cloud-native infrastructure, distributed systems, and modern data engineering practices.

• 5+ years of experience in Data Engineering, including experience building and operating large-scale, low-latency data platforms handling more than 100M events per day.
• Strong hands-on experience managing data infrastructure in Kubernetes environments with Docker, Helm, and cloud-native tooling.
• Proven experience with Infrastructure as Code technologies such as Terraform, Ansible, ArgoCD, or equivalent solutions.
• Deep understanding of distributed systems, including storage architectures, transactions, query processing, and performance optimization.
• Experience operating open-source query engines such as Trino or Presto.
• Strong knowledge of object storage systems and open table formats, particularly Apache Iceberg.
• Experience designing and operating streaming and CDC systems using technologies such as Kafka, Redpanda, and Debezium.
• Hands-on experience with orchestration frameworks such as Airflow and ELT tools such as Airbyte.
• Strong programming skills in Python and SQL for building pipelines, automation, and platform tooling.
• Experience with Google Cloud Platform data services, including GCS, Cloud Build, Cloud SQL, Dataproc, or comparable cloud platforms.
• Ability to work effectively in a fast-paced, startup environment with changing priorities and complex technical challenges.
• Strong problem-solving skills, ownership mindset, and ability to collaborate with distributed teams.

Preferred qualifications include:

• Experience with semantic and metrics layers such as Cube, dbt, or Looker.
• Familiarity with transformation frameworks and reverse ETL solutions.
• Knowledge of data catalog and lineage platforms such as OpenMetadata or DataHub.
• Experience implementing data access controls and governance frameworks such as Apache Ranger.

Benefits:

• Competitive salary package with stock options.
• Comprehensive health benefits.
• Remote-first work environment with flexible working arrangements.
• One-time home office setup allowance of $500 USD for new hires.
• Monthly stipend of $150 USD through a company-provided payment card.
• Opportunity to work with a globally distributed team of engineers and technology professionals.
• Inclusive workplace culture focused on diversity, ownership, curiosity, and collaboration.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1