Lead Service Data Engineer

Job Description

The Service & Operational Insights organization is seeking a Lead Service Data Engineer to join our growing Service & Operational Data team. This candidate will build, scale, and operationalize the data platform that powers service reliability, operational visibility, and data-driven decision-making across technology operations, partnering closely with the Enterprise Architect, Service & Operational Data, and other key stakeholders to translate target-state architecture, canonical data models, and governance standards into robust, production-grade pipelines and data products.

Key Responsibilities

• Implement the target architecture: Translate the operational data architecture and roadmap (incidents, changes, problems, alerts/events, CMDB, SLOs/SLIs, capacity, cost) into concrete, well-engineered pipelines, implementing canonical schemas such as service topology, event normalization, and incident timelines.
• Own pipeline reliability and operations: Build batch and streaming pipelines ingesting data from ITSM, observability platforms, cloud logs/metrics/traces, CI/CD, and infrastructure systems — with strong monitoring, alerting, and on-call practices.
• Operationalize data quality and trust: Implement data contracts, validation rules, testing frameworks, and lineage tracking; enforce dataset SLAs/SLOs and drive remediation when breached.
• Deliver operational data products: Build and maintain curated datasets, semantic layers, and metrics definitions powering dashboards, analytics, and automation use cases (AIOps, correlation, anomaly detection).
• Drive engineering excellence and mentorship: Establish coding standards, CI/CD practices, and reference implementations; provide technical direction through design/code reviews and mentor team members.
• Partner cross-functionally: Work with the Architect and Service Reporting team to influence data model design; collaborate with SRE, IT Ops, Security, and Analytics stakeholders to prioritize high-value use cases (incident reduction, alert noise reduction, change failure rate, capacity planning).
• Implement governance and security controls: Build RBAC/ABAC enforcement, retention policies, encryption, and auditability into pipelines and platforms.
• Support the roadmap: Provide engineering estimates, identify technical risks, and help sequence delivery of the operational data roadmap.

Required Qualifications :

• Bachelor's degree in Computer Science, Information Technology, or related field, with 10 + years of experience as a Data Engineer working with complex, large-scale data
• Strong proficiency in SQL and Python, with proven experience designing and delivering production-grade batch and streaming data pipelines
• Experience building data products (curated datasets, semantic layers, metrics definitions) and applying security governance principles (RBAC/ABAC, encryption, retention, auditability)
• Familiarity with ITSM, observability, and cloud data sources (incidents, changes, alerts/events, CMDB, logs/metrics/traces), along with data quality practices (contracts, testing, lineage)
• Strong problem-solving skills with minimal supervision, excellent communication, and ability to manage multiple projects and deadlines in parallel.

Preferred Qualifications :

• Experience supporting AIOps, correlation, or anomaly detection use cases
• Familiarity with SLA/SLO enforcement frameworks for data platforms
• Experience with capacity planning, cost, and reliability engineering data domains
• Prior experience partnering with data architecture functions to shape canonical schemas and data models
• Experience with DevOps technologies such as Jenkins, CodePipeline, CloudFormation, Terraform, Ansible

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What’s in it For You?

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