• Design, build, and maintain backend systems and APIs that power Wikidata’s query infrastructure • Improve reliability, observability, and automation of the Wikidata Query Service and data pipelines • Integrate Wikidata Platform services with other systems and data sources across Wikimedia • Collaborate with SRE, data engineers, and product teams to ensure stability and scalability under growing usage • Monitor production systems, respond to operational incidents, and proactively identify and resolve bottlenecks • Support platform migrations and system upgrades • Contribute to deployment automation, CI/CD workflows, and service instrumentation • Participate in code reviews, design discussions, and technical planning • Document systems and share knowledge with team members and Wikimedia’s broader technical community

Role Description We are looking for a Senior Platform Engineer to be the founding member of Burq's Platform team — the function that owns our engineering foundations: infrastructure, security, CI/CD, and reliability. - Own and execute the AWS account split — separating production, staging, and development into clean isolated environments - Lead the Aurora DB production migration with support from our infrastructure advisor - Manage and evolve our ECS Fargate services, RDS clusters, ElastiCache, SQS queues, and ALB configuration - Own Terraform — infrastructure as code, not click-ops - Harden IAM — developer permission sets, production access controls, least-privilege enforcement - Audit and lock down public S3 buckets - Enable AWS Config and Security Hub across all accounts - Own SOC 2 Type 2 readiness maintenance in partnership with Sprinto - Coordinate external penetration test cadence - Own and improve our GitHub Actions CI/CD pipelines — build times, test reliability, deployment velocity - Build out PR stack environments so product teams can test independently - Drive the local development stack initiative — Docker Compose-based environments that replace shared preprod - Integrate AI-assisted testing into the CI pipeline - Own CloudWatch dashboards spanning ALB, ECS, RDS, ElastiCache, SQS, and target group health - Lead post-incident reviews and ensure every incident produces a runbook entry - Partner with product teams to distribute platform work as a standing sprint allocation — not a separate queue Qualifications - 5+ years of engineering experience with 3+ years focused on infrastructure and platform engineering - Deep AWS expertise — ECS, RDS, S3, IAM, CloudWatch, SQS, Lambda, account architecture - Terraform proficiency — you write infrastructure as code and know why it matters - Strong CI/CD background — GitHub Actions, deployment pipelines, environment management - Security-minded — you know how to harden systems without creating friction for product engineers - Experience with observability tooling — CloudWatch, Datadog, or equivalent Benefits - Fully Remote - Medical, Vision and Dental Insurance - Reimbursement for educational courses Company Description At Burq, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

• Design and architect unified testing frameworks that enable seamless portability of test cases across Software-in-the-Loop (SIL), Hardware-in-the-Loop (HIL), and simulation environments. • Build, configure, and maintain physical HIL benches and associated hardware interfaces (e.g., CAN adapters) to simulate real-world vehicle environments. • Integrate automated HIL testing directly into the Continuous Integration (CI) pipelines, ensuring code stability and preventing regressions prior to merging. • Collaborate closely with software engineering teams to understand their needs, identify and implement improvements in our Hardware-in-the-Loop (HIL) Infrastructure and Continuous Integration (CI) systems. • Monitor and report on runtime performance, system health, and reliability metrics across all HIL testing platforms.

• Design, build, and operate scalable Data & AI platform capabilities for analytics, data science, and Generative AI use cases with strong practices around data quality, lineage, observability, security, and governance. • Develop reusable abstractions, templates, services, and workflows that standardize how teams build and manage data pipelines and data products. • Partner with Analytics, Applied-AI, Data-engineering and application teams to deliver platform capabilities that support business and clinical decision-making • Evaluate and integrate modern data and AI technologies with a focus on scalability, reliability, developer experience, and cost efficiency • Apply Generative AI technologies to improve data enablement, documentation, discovery, workflow automation, and platform usability • Promote software engineering best practices, including clean code, automated testing, CI/CD, infrastructure-as-code, monitoring, and secure data handling