{"id":3605905,"url":"https://alion.io/job/group-amana-data-engineer","title":"Data Engineer","company":{"id":4494557,"name":"Group AMANA","domain":"groupamana.com","url":"https://alion.io/company/group-amana","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":94,"open_postings":22,"ghost_share":0,"stale_share":0.227,"repost_share":0.045,"time_to_fill_p50_days":38,"computed_at":"2026-11-05T05:54:30Z"}},"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":["Dubai, United Arab Emirates"],"countries":["AE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Oracle ERP","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SharePoint","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-29T05:12:13Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-05T05:55:40Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"Data Engineer\nData Product Design & Delivery\nDesign and deliver end-to-end data products for assigned business domains across the bronze, silver, and gold layers, including the dimensional and semantic models that serve reporting and AI.\n\nBuild robust ingestion from Amana's enterprise systems - Oracle Fusion (Finance, HCM, P2P, CRM), Hyperion, CMiC, project and operations systems, SharePoint, and Power Platform - using change data capture, incremental, and API-based patterns.\n\nLead workstreams within the Synapse Analytics to Databricks migration: re-engineering pipelines, running parallel loads, reconciling results, and preparing for cutover.\n\nContribute reusable frameworks, templates, and patterns, and propose improvements to the standards owned by the Data Engineering Lead (Data Architect).\n\nData Quality, Governance & Security\nImplement data quality frameworks, monitoring, and reconciliation across your domains, and drive issues to resolution at source with data owners.\n\nImplement access control, lineage, classification, and data-handling rules in Unity Catalog and Microsoft Purview, including PDPL controls for personal and HR data.\n\nPartner with the Data Governance Office to turn domain policies into working controls and documented, certified datasets.\n\nPerformance, Reliability & Cost\nTune Spark and SQL workloads for performance and cost within the platform's cluster and workload policies.\n\nOwn the operational health of your domains: monitoring, alerting, incident response, and root-cause fixes.\n\nApply CI/CD, automated testing, and DataOps practices using Git and Azure DevOps.\n\nAnalytics & AI Enablement\nBuild AI-ready data products - feature tables and document and vector pipelines - for machine learning, RAG, and agent use cases, in partnership with the Data Scientist and AI Engineer.\n\nDeliver Power BI semantic models and certified datasets with the Data Analyst, supporting self-service adoption through the Amana Digital Academy.\n\nApply AI-assisted engineering - code generation, testing, documentation, and lineage discovery - with appropriate human review, and help the team adopt it safely.\n\nCollaboration & Mentoring\nTranslate requirements from finance, project, and operations stakeholders into technical designs with clear delivery outcomes.\n\nMentor Data Engineers through pairing, design reviews, and code review.\n\nExplain design choices, trade-offs, and risks clearly to technical and business audiences.\n\n Required Qualifications\n5+ years of hands-on data engineering experience, including at least 2 years with Databricks, Apache Spark, and Delta Lake in production.\n\nDesigned and delivered end-to-end data products - from ingestion to modelled, consumable data - for one or more business domains.\n\nExperience integrating complex enterprise sources such as Oracle Fusion, SAP, Microsoft Dynamics, or CMiC, including incremental and API-based extraction.\n\nData quality and access controls implemented in practice using Unity Catalog, Microsoft Purview, or equivalent tooling.\n\nStrong SQL, Python, and PySpark, with Git-based CI/CD delivery (Azure DevOps or equivalent).\n\nExcellent written and verbal communication skills across technical and business audiences.\nRequired (held, or achieved after joining within the period shown):\n\nDatabricks Certified Data Engineer Associate or Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) - within 6 months\n\nDatabricks Certified Data Engineer Professional - within 12 months\n\nPreferred (one or more):\n\nDatabricks Certified Generative AI Engineer Associate\n\nMicrosoft Certified: Fabric Data Engineer Associate (DP-700)\n\nMicrosoft Certified: Fabric Analytics Engineer Associate (DP-600)\n\nMicrosoft Certified: Azure Data Engineer Associate (DP-203 - 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