{"id":2917280,"url":"https://alion.io/job/exavalu-data-bi-lead","title":"Data & BI Lead","company":{"id":3802017,"name":"Exavalu","domain":"exavalu.com","url":"https://alion.io/company/exavalu","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":91000,"max_usd":185000,"period":"year","method":"global_role_seniority_cell","sample_n":631},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"Spark","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-20T10:54:34Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-21T12:47:39Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"This is a remote position.\nOverview :\nWe are seeking a Data & BI Lead with 10+ years of experience across data engineering, data warehousing, and business intelligence. This role requires strong hands-on expertise in modern cloud data platforms such as Microsoft Fabric, Azure Databricks, or Snowflake, along with deep experience in Power BI for building enterprise-grade analytical solutions.\nThe ideal candidate will lead and deliver end-to-end data platforms and BI solutions, with equal depth in data engineering, data modelling, and BI engineering (Power BI)\nKey Responsibilities:\nLead design and implementation of scalable data platforms (lakehouse / warehouse) on Microsoft Fabric, Azure Databricks, or Snowflake\nBuild and optimize data ingestion, transformation, and ELT pipelines for large-scale data processing\nDesign and implement enterprise data models across: Data warehouse / lakehouse layers\nAnalytical / semantic layers for reporting\n\nDevelop and maintain Power BI datasets, semantic models, and dashboards aligned with enterprise standards\nDrive query and performance optimization across: SQL workloads and data pipelines\nBI semantic layer and reporting performance\n\nEstablish data warehousing standards, modelling frameworks, and reusable data assets\nEnsure data quality, governance, lineage, and security across the platform\nCollaborate with stakeholders to translate business requirements into scalable data and analytics solutions\nProvide technical leadership across both data engineering and BI workstreams\nDrive DevOps and CI/CD practices for data pipelines and BI deployments\nRequirements\nRequired Skills & Experience:\n10+ years in Data Engineering, Data Warehousing, BI, or Analytics\nStrong hands-on expertise in data engineering on at least one platform (mandatory): Microsoft Fabric (Lakehouse, Dataflows, Pipelines)\nAzure Databricks (PySpark, Delta Lake, distributed processing)\nSnowflake (ELT design, performance optimization, data modelling)\n\nVery strong SQL expertise (mandatory):Complex transformations, query optimization, and performance tuning\nExperience handling large-scale, high-volume datasets\n\nDeep expertise in Data Modelling (mandatory):Dimensional modelling (star/snowflake schema)\nNormalization vs denormalization strategies\nSemantic layer design for analytics\n\nStrong Data Warehousing experience (mandatory):Designing and implementing enterprise-grade data warehouses\nStrong understanding of Kimball/Inmon methodologies\nExperience with lakehouse architectures\n\nStrong Power BI experience (mandatory):End to end development expertise\nBuilding semantic models, reports, and dashboards\nAdvanced DAX and Power Query (M)\nPerformance tuning (aggregations, incremental refresh, composite models)\n\nExperience with: Data orchestration tools (e.g., Azure Data Factory / Synapse or equivalent)\nPython / PySpark for data processing\nCI/CD and DevOps practices for data and analytics solutions\nData governance, lineage, and security (including RLS/OLS)\n\nGood to Have:\nExposure to AI / GenAI / Agentic AI concepts in data and analytics workflows\nExperience with Tableau\nIdeal Candidate Profile:\nStrong foundation in data engineering, data modelling, and BI engineering\nCapable of designing and building both backend data platforms and frontend analytical solutions\nComfortable working across architecture, development, and delivery\nEffective in engaging with both business stakeholders and technical teams\nFocused on scalability, performance, and long-term maintainability\nBenefits\nDiversity Inclusion: Exavalu also promotes flexibility, adapting to the needs of employees, customers, and the business. It might be part-time work, working outside normal 9-5 business hours or working remotely. We also offer a welcome back program to help individuals return to the mainstream after a prolonged absence due to health or family reasons. At Exavalu, we are committed to building a diverse and inclusive workforce. We welcome applications for employment from all qualified candidates, regardless of race, colour, gender, national or ethnic origin, age, disability, religion, sexual orientation, gender identity or any other status protected by applicable law. 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