Data Engineer Specialist
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Requirements
- Experience: 4-6 years of experience in data engineering or a related field.
- Bachelor's degree or equivalent practical experience.
- Proficiency in Python, SQL, GCP, and Lookerstudio.
- Exceptional attention to detail with a proven ability to maintain high quality on repetitive tasks and detailed workflows.
- Excellent reading comprehension skills and comfort acting independently to follow detailed workflows and escalate when necessary.
- Having as many of these specific qualifications is a plus, but transferable skills/experiences may be equally valuable:
- Experience with PLX/Looker, MySQL, Spanner, and Django.
- Strong problem solving, debugging, and troubleshooting skills.
- Ability to pick up skills on a new tech-stack rapidly.
- Ability to interact professionally with all levels of internal stakeholders
Benefits
Additional Information
Join Us! At Google Operations Center we help Google users and customers solve problems and achieve their goals-all while enjoying a culture focused on improving continuously and being better together. We work hard, we play hard, and we want you to join us! As a Data Engineer Specialist, you will be a key player in maintaining data integrity and ensuring the reliability of our critical reporting tools and dashboards. Position Responsibilities: Strategic Scoping: Collaborate with stakeholders to define data architecture, security protocols, and scalable access layers during the initial solution intake and evaluation phase. Technical Onboarding: Conduct comprehensive feasibility audits and system mapping during knowledge transfer to identify architectural bottlenecks and ensure seamless long-term operational integration. Pipeline Engineering: Architect high-performance ETL/ELT/ELTL workflows and automated data ingestion layers, delivering sophisticated business intelligence through advanced visualization and impact measurement. Incident Governance: Design an automated incident management framework to triage complex failures, optimizing manual runbooks into self-healing scripts for rapid resolution. Predictive Reliability: Implement proactive data observability using telemetry and Gemini-driven insights to preemptively mitigate latency, error rates, and potential component failures. End-to-End Sovereignty: Assume full orchestration ownership post-stabilization, overseeing continuous performance tuning, system health, and strict adherence to global legal compliance standards.
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