Senior Data Engineer, Reliability & Observability

Role Overview

• Architect and own scalable, maintainable, and extensible data models for reliability, automation, lab, and telemetry-generated data.
• Lead the design and evolution of database schemas that support cross-test analytics, traceability, repeatability, and long-term platform growth.
• Design and implement robust ingestion and transformation pipelines across automated test systems, CI/CD workflows, lab infrastructure, and supporting engineering tools.
• Define and standardize shared identifiers, metadata strategies, and data contracts that enable reliable correlation across runs, sessions, devices, builds, environments, and programs.
• Design complex or novel data solutions in areas where standards, tooling, or historical patterns are limited, fragmented, or outdated.
• Provide technical guidance and advisory support to reliability engineers, lab engineers, software/test automation engineers, and DevOps partners on data architecture, observability design, and scalable reporting patterns.
• Influence and help establish engineering standards for schema design, ingestion patterns, telemetry structure, data governance, and dashboard consumption.
• Enable observability workflows by structuring and integrating metrics, logs, events, and related telemetry into fit-for-purpose systems that support both operational debugging and strategic analysis.
• Support the development of dashboards and reporting experiences for both technical users and leadership stakeholders, including engineering deep dives, program health reporting, and executive-level views.
• Drive improvements in data quality, performance, consistency, integrity, and usability across the PSR ecosystem.
• Identify architectural bottlenecks and lead remediation strategies across current data storage, schema design, pipeline reliability, and visualization workflows.
• Create and maintain documentation, standards, and best practices for schema design, ingestion patterns, data governance, and dashboard enablement.
• Mentor and influence other engineers through design reviews, technical recommendations, and practical guidance on scalable data solutions.
• Contribute to and help shape a longer-term data and observability strategy that scales with evolving Pod programs, new test types, and future platform needs.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related technical field required; Master’s degree preferred.
• 7+ years of experience in data engineering, analytics engineering, platform engineering, or related roles; or 5+ years of experience with an advanced degree in a related field.
• Strong experience architecting relational database schemas and modeling structured engineering or operational data for scale, maintainability, and long-term reuse.
• Deep SQL expertise and hands-on experience with relational databases such as PostgreSQL or equivalent platforms.
• Strong experience building and evolving ETL/ELT or other data ingestion and transformation pipelines in production environments.
• Experience using Python or another programming language for data processing, automation, or integration tasks.
• Strong understanding of observability and telemetry concepts, including metrics, logs, events, and time-series data.
• Experience creating scalable reporting and visualization solutions in tools such as Grafana, Power BI, Tableau, or similar platforms.

Tech Stack

• ETL
• Grafana
• Postgres
• Python
• SQL
• Tableau

Benefits

• Medical, dental, and vision insurance
• 401(k) with company match
• Paid time off (PTO)
• And additional employee wellness programs