Summary
What you’ll impact
The Senior Data Platform Engineer at the organization will design and maintain secure cloud‑based data infrastructure, build and operate resilient real‑time and batch data pipelines, and ensure observability, performance, and governance of the platform. The role collaborates cross‑functionally with data professionals, developers, DevOps, and client delivery teams to support application workflows and operationalize AI models.
Responsibilities
What you'll do
- Architect & provision secure data infrastructure: Design, provision, and maintain cloud-based data storage and platform infrastructure using infrastructure-as-code (e.g., Terraform, Helm), optimizing schemas for multi-tenant environments while ensuring security and a high standard of trust and transparency
- Engineer and deploy data pipelines: Develop, deploy, and maintain resilient pipelines for both real-time streaming and batch processing, with deployment (CI/CD, IaC) as a top priority, ensuring seamless data flow from raw ingestion to production-ready applications
- Monitor and optimize infrastructure: Build monitoring and alerting systems to track data health and system performance, proactively identifying and remediating bottlenecks
- Support application workflows: Partner directly with the application team to troubleshoot database queries, pipeline logic, and data-related application issues as they come up
- Drive data governance and best practices: Contribute across teams to recommend tools, processes, and best practices for maintaining data health, integrity, and security
- Operationalize AI models: Support AI operations (MLOps) by managing versioning, containerization, and deployment of AI models
Requirements
What you’ll bring
- Experience: 5+ years of experience in a data-driven role
- Education: Bachelor’s or Master's degree in science, mathematics, engineering, or a data-driven field
- Core engineering: A strong foundation in Python (or equivalent) for scripting, automation, and tooling across the data platform
- Data architecture: Proven experience architecting scalable relational and non-relational (SQL/NoSQL) schemas
- Performance engineering: Expertise in maximizing system performance through advanced query tuning, strategic indexing, and execution plan analysis to eliminate technical bottlenecks
- Cloud & platform infrastructure: Hands-on experience with AWS data services (e.g., RDS, Aurora, Lambda) or Azure equivalents, plus infrastructure-as-code (Terraform and/or Helm) for provisioning and deploying data infrastructure
- Container orchestration & monitoring: Working knowledge of container build and orchestration tools (e.g., Kubernetes, Docker), and container-level observability (e.g., resource allocation, cluster health, container insights)
- Data orchestration: Experience building resilient pipelines using frameworks such as Dagster or Apache Airflow
- Systems thinking: A strong understanding of how data infrastructure integrates into the broader application architecture
- Professional standards: Experience with modern software development practices, including version control (Git), CI/CD pipelines, and infrastructure-as-code, with a demonstrated track record of deploying production systems