{"id":2398303,"url":"https://alion.io/job/pm-consulting-google-cloud-data-engineer","title":"Google Cloud Data Engineer","company":{"id":4421389,"name":"PM Consulting","domain":"pmconsulting.ph","url":"https://alion.io/company/pmconsulting-2","size_band":"1001-5000","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Taguig, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":49000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2157},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Kubernetes","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-12T22:49:46Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-14T04:54:37Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Position Overview\nWe are seeking an experienced Data Engineer to design, build, and maintain scalable data platforms and processing solutions within a Google Cloud environment. The role involves translating business requirements into reliable, secure, and high-performing data solutions that support analytics, reporting, and data-driven initiatives across the organization.\n\nKey Responsibilities\nDesign, develop, and maintain scalable ETL/ELT pipelines using Google Cloud technologies.\nBuild and optimize data models and warehouse structures to support large-scale analytical workloads.\nImplement and support both batch and real-time data ingestion frameworks.\nApply DataOps practices to improve data quality, monitoring, testing, and operational efficiency.\nDevelop and maintain CI/CD processes for data platform deployments.\nAutomate infrastructure provisioning and management using Infrastructure as Code (IaC) methodologies.\nMonitor, troubleshoot, and optimize production data environments to ensure performance, availability, and reliability.\nCollaborate with cross-functional stakeholders, including engineering, analytics, and business teams, to deliver data solutions.\nEnsure adherence to security, governance, compliance, and data protection standards.\nSupport containerized workloads and orchestration platforms where required.\nContribute to the continuous improvement of data architecture, engineering standards, and platform capabilities.\n\nQualifications\nExperience\nMinimum of 5-8 years of experience in Data Engineering, Cloud Engineering, or related disciplines.\nProven experience delivering end-to-end data solutions in a Google Cloud Platform environment.\nExperience working with enterprise-scale data platforms and complex data ecosystems.\nPreferred Certifications\nProfessional-level Google Cloud certifications in Data Engineering, Cloud Architecture, DevOps, or Application Development are advantageous.\nTechnical Requirements\nCloud and Platform Expertise\nStrong hands-on experience with Google Cloud data services, including data warehousing, data processing, orchestration, and messaging technologies.\nUnderstanding of cloud networking concepts such as virtual networks, subnetting, load balancing, and firewall configurations.\nKnowledge of cloud security principles and best practices for data environments.\nEngineering and Automation\nExperience implementing CI/CD pipelines for data engineering solutions.\nHands-on experience with Infrastructure as Code tools, such as Terraform.\nFamiliarity with containerization and orchestration technologies, including Docker and Kubernetes.\nProficiency in source code management and version control practices using Git.\nData Engineering Practices\nStrong understanding of DataOps principles and automated data quality processes.\nExperience designing and supporting high-volume, enterprise-scale data pipelines.\nExposure to regulated or highly governed environments is an advantage.\nKey Competencies\nStrong analytical and problem-solving skills.\nAbility to work effectively in cross-functional teams.\nExcellent communication and stakeholder management capabilities.\nCommitment to delivering scalable, reliable, and secure data solutions.","description_format":"text","description_chars":3197,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-12T22:49:46Z"}],"visa":[],"liveness":{"score":52,"band":"ok","label":"Likely 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