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 United States.**
• 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.