Lead Service Data Engineer

Role Overview

Key Responsibilities

• Implement operational data architecture and roadmaps as pipelines and canonical schemas for service topology, events, and incident timelines.
• Build reliable batch and streaming pipelines from ITSM, observability, cloud telemetry, CI/CD, and infrastructure systems, with monitoring, alerting, and on-call practices.
• Implement data contracts, validation, testing, and lineage; enforce dataset SLAs/SLOs and remediate breaches.
• Deliver curated datasets, semantic layers, and metrics definitions for dashboards, analytics, and automation such as AIOps and anomaly detection.
• Establish coding standards, CI/CD practices, and reference implementations; provide technical direction, review designs and code, and mentor team members.
• Partner with architecture and reporting teams on data models and collaborate with SRE, IT Ops, Security, and Analytics on use cases such as incident reduction, alert-noise reduction, change failure rate, and capacity planning.
• Implement RBAC/ABAC, retention, encryption, and auditability controls in pipelines and platforms.
• Estimate engineering work, identify risks, and help sequence the operational data roadmap.

Required Qualifications

• Requires a bachelor's degree in Computer Science, Information Technology, or a related field and 10+ years of data engineering experience with complex, large-scale data.
• Requires strong SQL and Python proficiency and production-grade batch and streaming pipeline experience.
• Requires experience building data products and applying security governance principles, including RBAC/ABAC, encryption, retention, and auditability.
• Requires familiarity with ITSM, observability, cloud data sources, and data quality practices such as contracts, testing, and lineage.
• Requires problem-solving with minimal supervision, communication skills, and ability to manage concurrent projects and deadlines.

Preferred Qualifications

• AIOps, correlation, or anomaly detection experience is preferred.
• Familiarity with data platform SLA/SLO enforcement is preferred.
• Capacity planning, cost, and reliability engineering data domain experience is preferred.
• Experience partnering with data architecture functions on canonical schemas and data models is preferred.
• Experience with Jenkins, CodePipeline, CloudFormation, Terraform, or Ansible is preferred.

Workplace

• The employer describes a flexible hybrid model, with office attendance 2–3 days per week depending on the role.
• The employer also describes work-from-anywhere flexibility for up to 8 weeks per year.

Benefits and Flexibility

• The description promotes flexible work-life policies, career development, continuous learning, and skills development.