Data Platform Operations Engineer – Performance

We are looking for a data-focused engineer with a strong operational focus to support the Performance Team’s data platform at IMC. This role is responsible for the reliability, correctness, and performance of data systems that directly support performance analysis and decision-making.

The role is production-facing and works closely with the Performance team and aligned stakeholders to ensure data workflows are robust, observable, and efficient.

What you’ll do:

• Own the operational health of the Performance Team’s data platform
• Monitor and maintain ETLs and data pipelines for correctness, timeliness, and stability
• Design and maintain data quality checks, reports, and alerting specific to performance data
• Investigate and resolve data issues and pipeline failures, driving problems to root cause
• Own and troubleshoot GitLab CI/CD pipeline issues related to performance data workflows
• Monitor workloads and resource usage; identify and resolve imbalances and bottlenecks
• Build and maintain operational tooling, automation, and runbooks
• Continuously tune data pipelines and workloads to improve performance and resource efficiency

What you bring:

• 3+ years of experience in software engineering or data engineering with an operational or production focus
• A Bachelor’s Degree in Computer Science or a related field is strongly preferred
• Proficiency in Python, with hands-on experience using PySpark and Pandas
• Experience supporting production ETL or data processing systems
• Familiarity with Git-based development workflows and CI/CD systems (GitLab preferred)
• Strong analytical skills and attention to detail
• Comfortable taking ownership in a performance- and reliability-critical environment

Nice to Have

• Experience working with performance-critical or high-throughput systems
• Exposure to workload scheduling, resource management, or cluster-based compute

What Success Looks Like

• Performance data pipelines are reliable, observable, and trusted
• Issues are detected early and resolved quickly with clear root-cause ownership
• Workloads are well-balanced and systems run efficiently