{"id":3082645,"url":"https://alion.io/job/lenovo-lps-senior-data-engineer","title":"[LPS] Senior Data Engineer","company":{"id":15044,"name":"Lenovo","domain":"lenovo.com","url":"https://alion.io/company/lenovo","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":100,"open_postings":11,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":11,"computed_at":"2026-10-30T05:48:23Z"}},"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":["Hong Kong"],"countries":["HK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":57000,"max_usd":150000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-22T12:39:56Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-30T14:57:44Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"Lenovo PCCW Solutions (LPS) AI Data Intelligence Practice helps enterprises become truly AI-ready through data strategy, AI readiness, data governance, modern data platforms, analytics, generative AI and managed services. The practice delivers end-to-end services across Strategy, Build and Adopt, helping clients turn enterprise data into trusted business assets and AI-ready foundations.\nWe work with leading platforms and ecosystems including Microsoft Fabric, Databricks, Azure, AWS, Alibaba Cloud, Lenovo Hybrid AI, modern data governance platforms, data product frameworks, and enterprise AI technologies.\nOur clients span financial services, government and public sector, manufacturing, education, retail, hospitality, property, logistics and enterprise markets across Hong Kong and Greater China.\n\nRole Overview:\nAs a Senior Data Engineer, you will design, build and optimize secure, scalable and reliable data pipelines that power analytics, AI and enterprise reporting workloads. You will work across batch, streaming, API and cloud-native integration patterns.\nKey Responsibilities:\n\nData Engineering\nDesign and develop ETL/ELT pipelines for enterprise data platforms.\nBuild batch, CDC, streaming and API-based data integration solutions.\nImplement data validation, data quality, reconciliation and error handling frameworks.\nCreate reusable data engineering components, templates and accelerators.\nSupport data modelling, transformation and data serving requirements.\n\nPlatform Engineering\nDevelop cloud-native data solutions on modern data platforms.\nSupport data platform migration, modernization and performance optimization initiatives.\nImplement monitoring, logging, alerting and operational observability for data pipelines.\nWork with architects to translate solution designs into robust engineering deliverables.\nEngineering Excellence\nApply DataOps, DevOps, CI/CD and version control best practices.\nOptimize data workloads for scalability, reliability and cost efficiency.\nDocument technical designs, deployment procedures and operational runbooks.\nRequired Qualifications:\nBachelor's degree in Computer Science, Engineering, Information Systems, Data Analytics or a related discipline.\n3-8 years of experience in data engineering, ETL development, data platform implementation or cloud data projects.\nStrong SQL and Python skills.\nExperience with Spark, Databricks, Microsoft Fabric, Azure Data Factory, Kafka, Airflow or equivalent tools.\nGood understanding of data integration, data quality and pipeline orchestration concepts.\nPreferred Qualifications\nExperience with Git, CI/CD, Terraform, Docker or cloud-native deployment practices.\nExposure to streaming, CDC, DataOps, cloud migration or lakehouse implementations.\nExperience supporting enterprise analytics, AI, reporting or managed service environments.\nWhat Success Looks Like\nPipelines are reliable, secure, observable and easy to operate.\nData is delivered where it is needed with quality, timeliness and scalability.\nEngineering patterns become reusable assets across multiple client engagements.\n\nWorking Style\nCollaborate with business stakeholders, architects, engineers, consultants and service delivery teams.\nOperate in a consulting-led delivery environment with strong ownership of outcomes.\nContribute to reusable methodology, accelerators and solution assets for the practice.\nSupport enterprise customers in Hong Kong with occasional regional collaboration as required.\nWhy Join Us\nJoin a growing AI Data Intelligence Practice focused on strategy, platform, analytics, AI and managed services.\nWork on enterprise-scale transformation programs with modern data and AI platforms.\nDevelop cross-disciplinary capability across consulting, architecture, engineering, data products and adoption.\nHelp clients build AI-ready data foundations that support BI, ML, Gen BI and AI 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