Senior Data Platform Engineer
Skills
About the Role
You will own the day-to-day operation and monitoring of our data platforms and pipelines, driving continuous improvements from an operational and maintenance perspective. You will work closely with data engineers, analysts, and platform teams to ensure our data platforms are stable, observable, cost-efficient, and ready to support new use cases. You will handle incidents, troubleshoot failed jobs, implement observability practices, automate recurring tasks, and contribute to operational documentation and knowledge sharing.
Requirements
- Hands-on experience in data platform operations, data engineering, or a related role
- Strong understanding of data processing concepts (batch, scheduling, dependencies, failures, backfills)
- Practical experience with Azure data services and/or traditional ETL tools
- Comfortable working with SQL and modern version-control workflows (branches, pull requests, code reviews)
- Problem-solving mindset with a focus on stability, reliability, and continuous improvement
- Good communication skills; able to work with both technical and non-technical stakeholders
- Experience with or interest in a modern data stack: AWS, Snowflake, dbt / SQLMesh, Git / GitHub, CI/CD, Kubernetes, Kafka, IaaC and platform automation
- Scripting / programming skills for automation and tooling: SQL / Procedural SQL, Python, Bash
- Ability to build small tools and utilities to automate operational tasks, improve monitoring, or integrate systems
Responsibilities
- Operate and monitor data platforms (Azure and on-prem / legacy) to ensure availability, performance, and reliability
- Run and support data processing workflows, including incident handling, troubleshooting failed jobs, and coordinating fixes with development teams
- Implement and improve observability (monitoring, alerting, logging, SLAs/SLOs) for data platforms and pipelines
- Simplify and automate recurring operational tasks
- Reduce manual interventions and operational risk
- Improve deployment, rollback, and change-management practices
- Collaborate with data engineers and product teams to operationalize new data products and integrate them into existing platform standards
- Contribute to operational documentation (runbooks, standards, best practices) and knowledge sharing across the data organization