Description
The Service & Operational Insights organization is seeking a Lead Service Data Engineer to join the growing Service & Operational Data team. This role involves building, scaling, and operationalizing the data platform that powers service reliability, operational visibility, and data-driven decision-making across technology operations.
Key Responsibilities:
- Implement the target architecture: Translate the operational data architecture and roadmap into concrete, well-engineered pipelines.
- Own pipeline reliability and operations: Build batch and streaming pipelines ingesting data from various systems.
- Operationalize data quality and trust: Implement data contracts, validation rules, testing frameworks, and lineage tracking.
- Deliver operational data products: Build and maintain curated datasets, semantic layers, and metrics definitions.
- Drive engineering excellence and mentorship: Establish coding standards, CI/CD practices, and reference implementations.
- Partner cross-functionally: Work with the Architect and Service Reporting team to influence data model design.
- 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.
- Strong proficiency in SQL and Python, with proven experience designing and delivering production-grade batch and streaming data pipelines.
- Experience building data products and applying security governance principles.
- Familiarity with ITSM, observability, and cloud data sources.
- 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.
What's in it For You?:
- Hybrid Work Model: Flexible hybrid working environment.
- Flexibility & Work-Life Balance: Supportive workplace policies.
- Career Development and Growth: Culture of continuous learning and skill development.
- Industry Competitive Benefits: Comprehensive benefit plans.
- Culture: Globally recognized, award-winning reputation for inclusion and belonging.
- Social Impact: Opportunities to make an impact in the community.