Data Engineer Senior Specialist
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Requirements
- Experience: 6-8 years of experience in building and maintaining enterprise-level data applications.
- Bachelor's degree or equivalent practical experience.
- Proficiency in Python, SQL, GCP, and dashboarding /visualization tools ( Lookerstudio, Data Studio, Tableau or PowerBI)
- 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.
- 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 Senior Specialist in the Google Operations Center, you will drive technical excellence by designing and scaling complex data solutions. We are looking for a creative problem solver who can partner with stakeholders to ensure our data infrastructure is both resilient and impactful. 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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