{"id":3362757,"url":"https://alion.io/job/nagarro-principal-engineer-databricks-data-engineer","title":"Principal Engineer - Databricks Data Engineer","company":{"id":2457,"name":"Nagarro","domain":"nagarro.com","url":"https://alion.io/company/nagarro","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"A","score":85,"open_postings":36,"ghost_share":0,"stale_share":0.611,"repost_share":0,"time_to_fill_p50_days":25,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Guadalajara, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":127000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":771},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure Data Factory","optional":false},{"name":"Databricks","optional":false},{"name":"Google Workspace","optional":true}],"status":"live","first_seen_at":"2026-10-27T20:14:16Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-30T15:47:11Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale - across all devices and digital mediums, and our people exist everywhere in the world (15000+ experts across 26 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!\n Job Description :\nAudit the current Databricks workspace state, including environments, clusters, Unity Catalog setup, access controls, and cost configuration.\nImplement and harden Unity Catalog, including namespace structure, access controls, cluster policies, and environment separation. \nDefine workspace standards, naming conventions, access request workflows, and user/team onboarding processes.\nEstablish data quality frameworks, including SLAs, certification criteria, and data ownership models.\nBuild automation templates for routine workspace operations such as cluster lifecycle management, cost alerting, and utilization monitoring.\nStand up observability dashboards covering platform health, cost, job success rates, and DQ SLA KPIs, and monitor core data asset lineage across workspaces.\nCreate runbooks for recurring operational tasks that can be executed by a mid-level/junior engineer without ongoing support from the senior resource.\nConduct knowledge-transfer sessions with the platform engineering team.\nProvide final documentation covering architecture decisions, operational standards, open items, and recommendations for the permanent hire profile.\nKey Responsabilities.\nEvaluate and optimize Databricks environments, including workspaces, clusters, catalogs, permissions, and security configurations.\nDefine and implement governance standards to ensure secure, scalable, and compliant platform operations.\nLead Unity Catalog adoption, designing data structures, access models, and environment segregation strategies.\nEstablish RBAC policies, workspace standards, naming conventions, and user access processes.\nDrive data governance initiatives, including data quality frameworks, ownership models, SLAs, lineage, and stewardship practices.\nDevelop automation solutions and Infrastructure as Code (IaC) frameworks to streamline platform administration and reduce manual effort.\nImplement cost optimization, monitoring, and operational excellence practices across Databricks environments.\nDesign observability and performance dashboards to track platform health, utilization, job success rates, and data quality KPIs.\nCreate operational documentation, runbooks, and support procedures to improve platform reliability and team readiness.\nPartner with security, compliance, infrastructure, and data engineering teams to align platform capabilities with business needs.\nProvide training, mentoring, and knowledge transfer sessions to empower platform administrators and engineering teams.\nRecommend platform improvements and long-term strategies to enhance governance, scalability, and organizational maturity.\n Must have skills: Azure Data Factory, Databricks, Unity Catalog\nGood to have skills: Data platform implementation, Google Workspace 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