{"id":137828,"url":"https://alion.io/job/khibraty-data-analytics-director","title":"Data & Analytics Director","company":{"id":5400,"name":"Khibraty","domain":"khibraty.com","url":"https://alion.io/company/khibraty","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"D","score":45,"open_postings":46,"ghost_share":0.913,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Analytics","role_family":"Analytics","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Amman, Jordan"],"countries":["JO"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":85000,"max_usd":183000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":351},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Collibra","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Feature Store","optional":false},{"name":"MLFlow","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Power BI","optional":false},{"name":"Veeva","optional":false}],"status":"live","first_seen_at":"2026-06-30T00:00:00Z","employer_posted_date":"2026-06-30","last_verified_at":"2026-10-22T14:39:12Z","board_verified":true,"closed_at":null,"days_open":114,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":114},"description":"A leading global pharmaceutical company is seeking a Director, Data & Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data & Analytics function. This is a hands-on leadership role overseeing the enterprise data platform, the data products the business relies on, and the team behind them, setting technical direction, delivery standards, and investment priorities while staying personally engaged in key architecture and design decisions. The ideal candidate combines strong technical credibility with senior leadership experience, and will be directly accountable for making the platform AI-ready, ensuring data is governed, traceable, and model-grade to support regulated AI deployment.\nResponsibilities:\n1. Platform Architecture & Engineering Leadership:\nOwn the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardised, reliable, and engineered to scale\nSet and enforce platform engineering standards: ingestion patterns, transformation conventions, Medallion layer contracts, data quality gates, Bronze-to-Gold promotion criteria, and the CI/CD framework that delivers all of it\nOwn the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data\nDrive the platform roadmap from current state to target, sequencing technical debt remediation, new capability build-out, and platform readiness for downstream AI and analytics demand\nPersonally lead design reviews and architecture decisions for the platform, engaging directly with the engineering team on complex technical problems where senior technical judgment is required\nOwn data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring\n2. Delivery & Data Products:\nRun the D&A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions\nSet and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output\nDefine and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them\nOwn the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness\nManage complex integration challenges across source systems, engaging directly on technical constraints, working with upstream owners, and designing solutions that align data latency and refresh frequency with business needs\nOwn the portfolio of data products, datasets, semantic models, and analytics deliverables, ensuring each is documented, well-understood, and fit for the business question it answers\nDefine and own the standard for what a Gold-layer data product must satisfy to be AI-ready, distinguishing analytics-grade from model-training-grade, with explicit criteria covering completeness, label integrity, statistical consistency, and lineage traceability\n3. Data Governance & Quality:\nDesign and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions\nEmbed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths\nOwn data lineage visibility across the platform so business users can trace the provenance of every number they rely on, end-to-end from source to dashboard\nLead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platform metadata layer\nEmbed appropriate data handling, validation, and audit-trail practices for regulated data domains, partnering with Quality, Regulatory, and Legal to ensure compliance requirements are met by design\nEstablish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners\nEstablish data lineage and provenance practices that satisfy AI explainability and regulatory auditability requirements, including GxP-compliant audit trails for Quality, Manufacturing, and Regulatory AI use cases\n4. Team Leadership & Capability Development:\nLead, hire, and develop the D&A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand\nDefine and evolve the role design, skills profile, and career framework for the team within the broader Data & AI CoE structure\nSet the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline\nCoach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles\nManage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities\nSet the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output \n5. Business Partnership & Vendor Engagement:\nServe as the senior D&A point of contact for business function leadership, translating business data needs into platform and delivery priorities\nBuild credibility with business stakeholders through reliable, consistent delivery, data products that are well-understood, well-documented, and match business expectations\nCommunicate proactively on platform status, delivery commitments, risks, and trade-offs, ensuring stakeholders have a current view and surfacing issues early\nRepresent D&A in cross-functional planning forums, ensuring the data foundation perspective is present in enterprise architecture, application, and AI investment decisions\nManage operational vendor and partner relationships for the data platform, Databricks, Microsoft Azure, Power BI, and implementation or augmentation partners\nRequirements\nBachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline\nMaster's degree in Data Science, Computer Science, Business Administration, or related field is preferred\nRelevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred\nMinimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles\nMinimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team\nProven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level\nProven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment\nExperience with Medallion/lakehouse architecture patterns in production\nExperience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred\nExperience working at the interface of a data platform team and an AI/ML team, translating model requirements into data infrastructure specifications and owning the data readiness handoff is preferred\nExperience leading a data governance or catalogue implementation, from design through to business adoption\nLife sciences, pharmaceutical, or other regulated industry experience is preferred\nSkills:\nTechnical Competencies:\nData Platform Architecture\nData Engineering, Pipeline Design & CDC Patterns\nData Modelling, Transformation & Engineering Standards\nData Governance, Quality & Catalogue\nAnalytics & BI Delivery\nMLOps & ML Platform Foundations\nDelivery Management & Agile Methods\nPlatform & Technical Skills:\nDeep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps \nHands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow \nStrong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar)\nPower BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale\nCI/CD for data pipelines, practical implementation at production scale\nData quality frameworks, profiling, expectation testing, alerting, and remediation workflows\nWorking knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub \nLeadership & Business Skills:\nCredible with both technical teams and senior business stakeholders\nStrong delivery discipline, owns commitments, communicates risks early, and sizes work realistically\nStructured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap\nStrong written and verbal communication in English; 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