{"id":3526480,"url":"https://alion.io/job/ltm-senior-specialist-data-engineering-50","title":"Senior Specialist - Data Engineering","company":{"id":9353,"name":"LTM","domain":"ltm.com","url":"https://alion.io/company/ltm","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"RippleHire","truth_index":{"grade":"B","score":77,"open_postings":169,"ghost_share":0,"stale_share":0.917,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bellevue, United 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operational and compliance needs\nConvert business requirements into functional design sourcetotarget mappings transformation logic and technical specifications\nValidate requirements against enterprise data models and recommend architecture patterns Lakehouse Warehouse Realtime Hub\nData Modeling Fabric Semantic Layer\nDesign build and govern Fabric Semantic Models Direct Lake Import DQ Hybrid modes\nDefine enterprisewide canonical models shared dimensions hierarchies KPIs and reusable DAX measures\nOptimize semantic models for performance using aggregations incremental refresh and partitioning strategies\nEnable certified datasets semantic governance and rolelevel security within Fabric\nETLELT Engineering with Fabric Pipelines Dataflows Notebooks\nBuild ingestion and transformation processes using Data Factory pipelines Dataflows Gen2 Warehouse pipelines and PySpark notebooks\nMaintain metadatadriven ETL patterns and reusable frameworks for ingestion harmonization and transformation\nFabric Notebook Engineering\nUse Fabric Notebooks to perform\nPySpark transformations\nDelta Lake optimization Zorder vacuuming partitioning\nData validation and data quality checks\nML feature engineering and lightweight model operations\nAutomate notebook execution via pipelines triggers and Fabric scheduling\nIntegrate notebooks with Lakehouse tables Warehouse tables and ML model outputs\nNear RealTime NRT Data Processing in Fabric\nDesign and implement nearrealtime data ingestion pipelines using\nFabric RealTime Hub\nEvent Streams\nKQL Databases\nStreaming Dataflows\nBuild streaming transformations and realtime analytical models leveraging Kusto Query Language KQL and PySpark Structured Streaming\nEnsure lowlatency ingestion to LakehouseWarehouse for downstream consumption\nOptimize realtime workloads for durability recovery and performance under high throughput\nBuild dashboards and semantic models that support nearrealtime refresh scenarios\nPerformance Optimization\nOptimize SQL queries Lakehouse Delta tables semantic models DAX expressions and Power BI datasets\nReview and tune pipeline throughput notebook execution performance and refresh schedules\nImprove Direct Lake performance by optimizing storage layouts file size distributions and columns\nPerform workload monitoring using Fabric capacity metrics and logs\nPower BI Development Visualization\nBuild highquality Power BI dashboards and enterprise reports integrated with centralized semantic models\nDevelop DAX calculations KPIs UXUI standards drillthroughs and rowlevel security\nDrive semantic model reuse and promote governed gold datasets\nGovernance Security Compliance Purview Integration\nImplement data governance and cataloging using Microsoft Purview for Fabric assets Manage lineage tracking glossary management classification and metadata enrichment\nDefine enterprise security controls RBAC masking PII handling encryption retention policies\nEnsure compliance with GDPR CCPA HIPAA SOX and internal audit controls\nGovern Fabric workspace structure capacity usage data certification processes and lifecycle management\nTechnical Leadership/ Program Delivery\nLead data engineers BI developers and analysts across multiple initiatives\nReview designs STTMs code semantic models and performance benchmarks\nOwn sprint planning estimation milestone tracking and stakeholder communication\nPromote documentation technical standards reusable design frameworks and automation\nTechnical Expertise\n8 years of data engineering or BI experience with 2 years in Microsoft Fabric\nExpert in data warehousing design dimensional modeling semantic modeling and data governance\nStrong handson experience with\nFabric Lakehouses Warehouses\nFabric Semantic Models Direct Lake Import Hybrid\nRealTime Hub Event Streams KQL Databases\nNotebooks PySpark data transformations optimization\nData Factory pipelines Dataflows Gen2\nPower BI modeling DAX report development\nSolid 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