Lead Service Data Engineer at Thomson Reuters in Bengaluru

Job description

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 #LI-SM1 What’s in it For You? Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected. Flexibility & Work-Life Balance: Flex My Way is a set o