Senior Data Platform Engineer

Accountabilities: In this role, you will build and maintain critical data platform infrastructure while ensuring scalability, reliability, security, and performance across the organization's data ecosystem. Design, develop, and operate core data platform infrastructure, including distributed query engines, lakehouse storage, orchestration systems, and streaming pipelines. Build scalable and repeatable infrastructure for regional platform deployments using Kubernetes and infrastructure-as-code practices. Develop real-time data processing, analytics, and alerting capabilities to support operational and product requirements. Collaborate with AI and Data Science teams to establish platform foundations for agentic workflows, model evaluations, and machine learning lifecycle tooling. Improve data cataloging, governance frameworks, and access controls to ensure data remains discoverable, secure, and properly managed. Take ownership of platform reliability through observability, on-call participation, incident response, and continuous operational improvements. Work closely with cross-functional engineering teams to identify technical challenges and deliver effective platform solutions. Contribute to the evolution of distributed data infrastructure as transaction volumes, customer needs, and product capabilities expand. Drive technical improvements across platform architecture, deployment automation, performance, and operational efficiency. Requirements The ideal candidate is an experienced data or platform engineer with strong software development capabilities, a deep understanding of distributed systems, and the ability to take ownership of complex production infrastructure. At least 5 years of experience in data infrastructure, platform engineering, or software engineering, with demonstrated end-to-end ownership of production systems. Strong proficiency in Python and SQL, with experience developing reliable data pipelines, services, and platform tooling. Hands-on experience building or operating distributed data systems, including query engines, lakehouse architectures, or streaming platforms. Experience managing cloud infrastructure using Kubernetes, infrastructure-as-code, and automated deployment processes. Strong understanding of distributed systems, data processing architectures, and production infrastructure reliability. Sound operational judgment and the ability to troubleshoot complex technical challenges. Excellent technical communication and collaboration skills, with the ability to work effectively across distributed engineering teams. Ability to work independently, manage multiple initiatives, and take responsibility for projects from design through production. Nice to have: Experience with distributed query engines such as Trino, Presto, or Spark, and open table formats such as Apache Iceberg. Familiarity with streaming and real-time data technologies, including Kafka, Redpanda, Flink, or Debezium. Experience with AI and agent infrastructure, such as MCP servers, retrieval systems, agent observability, or LLM evaluation frameworks. Experience supporting machine learning infrastructure using MLflow, Vertex AI, or similar platforms. Knowledge of real-time analytics engines such as ClickHouse, Pinot, or Druid. Experience designing and operating infrastructure across multiple cloud regions, including workload isolation and cost-performance optimization. Benefits Competitive salary. Stock options. Health benefits. One-time USD $500 home-office setup allowance for new hires. Monthly USD $150 stipend provided through a Brex Card. Fully remote working environment. Opportunity to collaborate with a globally distributed team of experienced engineers and technology professionals. Exposure to large-scale financial infrastructure, modern data technologies, and innovative AI initiatives.