{"id":4225263,"url":"https://alion.io/job/dentsu-data-engineer-8","title":"Data Engineer","company":{"id":888,"name":"dentsu","domain":"dentsu.com","url":"https://alion.io/company/dentsu-polska","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":144,"ghost_share":0.014,"stale_share":0.729,"repost_share":0.042,"time_to_fill_p50_days":31,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ho Chi Minh City, Vietnam"],"countries":["VN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false}],"status":"live","first_seen_at":"2026-11-13T07:34:12Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T14:24:47Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"The Data Engineer is responsible for designing, building, and maintaining the company’s data infrastructure - from raw data ingestion through to production-ready datasets that serve both operational systems and AI workloads. A core focus of this role is building a comprehensive metadata and semantic layer that clearly describes every dataset, table, and field, enabling AI systems to scan, analyze, and generate reports autonomously. This role works closely with the technical team, AI team, project team, and clients to ensure data is reliable, well-documented, and ready for both human analysis and machine consumption.Job Description:\nThe Role\nData Pipeline Development\nDesign and build automated ETL/ELT pipelines to ingest data from multiple sources (Call Center, DMS, Social Platform, third-party APIs).\nDevelop scheduling, monitoring, and alerting mechanisms to ensure pipeline reliability and SLA compliance.\nImplement incremental processing, deduplication, and change data capture (CDC) strategies for high-volume data flows.\nMaintain and optimize existing pipelines for performance, cost, and maintainability.\nMetadata & Semantic Layer\nBuild and maintain a comprehensive metadata catalog with clear descriptions for every table and column across all datasets.\nDesign and implement a semantic layer that defines dataset purposes, field definitions, data types, business logic, and relationships between tables.\nEnsure the semantic layer is structured for AI consumption - enabling AI systems to understand data context, scan datasets, perform analysis, and generate reports without manual intervention.\nEstablish and enforce documentation standards so that every new dataset or schema change is accompanied by complete metadata.\nCollaborate with the AI team to validate that the semantic layer provides sufficient context for automated data exploration and insight generation.\nData Quality & Governance\nImplement automated data quality checks at each pipeline stage: completeness, accuracy, consistency, and freshness.\nBuild monitoring dashboards to track data health metrics and alert on anomalies or pipeline failures.\nEstablish data lineage tracking so that downstream consumers (reports, dashboards, AI models) can trace data to its source.\nCollaborate with stakeholders to define data ownership, access policies, and retention rules.\nAI Data Infrastructure\nPrepare and serve datasets for AI/ML workloads: feature stores, training datasets, and inference data feeds.\nCollaborate with the AI team to define data requirements for model training, evaluation, and monitoring.\nBuild and maintain data APIs or serving layers that enable AI systems to consume data in real time or near-real time.\nSupport the AI team in data profiling, exploratory analysis, and data readiness assessments for new AI use cases.\nCross-team Collaboration\nWork with the technical team on system integration and data flow architecture across platforms.\nCollaborate with the AI team to ensure data infrastructure meets the requirements of AI/ML workloads.\nSupport the project team and clients in understanding data availability, structure, and operational workflows.\nParticipate in client-facing discussions to gather data requirements and provide technical guidance on data solutions.\nLocation:\nHo Chi Minh CityBrand:\nMerkleTime Type:\nFull timeContract Type:\nFixed Term Contract (Fixed 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