{"id":2886724,"url":"https://alion.io/job/outsystems-senior-analytics-engineer","title":"Senior Analytics Engineer","company":{"id":177982,"name":"OutSystems","domain":"outsystems.com","url":"https://alion.io/company/outsystems-2","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":77,"open_postings":12,"ghost_share":0,"stale_share":0.917,"repost_share":0,"time_to_fill_p50_days":36,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lisbon, Portugal"],"countries":["PT"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":32000,"max_usd":62000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":15},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Cortex","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"LookML","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-10-19T00:00:00Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-22T17:43:57Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"There are NO limits to your career: come shape the future and be part of a truly unique global culture at OutSystems!\nAbout This Role\nAs a Senior Analytics Engineer within OutSystems’ centralized Data Area, you’ll join the Analytics Engineering team and and play a leading role in establishing and scaling a new strategic practice: the company’s governed Semantic Layer - the foundation for both AI agents and BI tools, enabling them to answer business questions consistently using shared definitions of facts, dimensions, metrics, and business concepts.\nYou'll sit at the intersection of analytical modeling, semantic modeling, and Conversational Analytics Agents enablement. Your role will involve translating OutSystems’ Canonical Data Model into trusted, governed, and reusable Semantic Models, ensuring that business logic and metrics are defined once, correctly, and consistently reused across different analytics and AI consumption experiences..\nAs one of the founding engineers of this new practice, you’ll define and implement the semantic modeling standards, development patterns, tooling, quality controls, and governance processes required to build the Semantic Layer from the ground up across OutSystems’ core business domains. As the capability matures and scales, the Semantic Layer will evolve from an initial founding initiative into an ongoing core function of the Analytics Engineering team. You’ll continue to extend its coverage as new business domains, data sources, metrics, and consumption patterns emerge.\nRole Context\nThis is a hands-on technical role requiring strong dimensional/semantic modeling craft combined with software-engineering discipline (version control, CI/CD, automated testing). You'll operate within a SWAT Team model, staffed per Semantic Model build alongside a Data Science & Analytics Analyst (semantic scope definition, agent development and testing) and a Business Stakeholder (answer validation). As the Analytics Engineer on the team, you'll own the Semantic Model end-to-end - from conceptual and logical design through physical build and deployment.\nYou'll also work closely with the Canonical Data team to ensure the Canonical Data Model remains the sole foundation for every Semantic Model - surfacing gaps or misalignments in the CDM early rather than compensating for them downstream - and to uphold our 3-layer architecture (Canonical → Semantic → Consumption), so that logic lives in the right layer and never gets duplicated or reintroduced at the reporting or agent level.\nYou'll work directly with these stakeholders to gather analytical and AI use cases, translate them into Semantic Model requirements, and ensure metrics and business definitions are accurate, consistent, and reused across domains rather than redefined model by model.\nYou'll operate inside the Data Area's Center of Excellence, following the same governed engineering discipline used for the Canonical Data Model: version-controlled development in a shared GitHub repository, dbt Core for transformation logic, pre-merge CI gates via GitHub Actions, and Airflow-orchestrated deployment into production.\nWhat You Will Lead/Do\nDesign and build Semantic Models on top of the Canonical Data Model, covering existing and future business domains as the semantic layer scales\nApply Kimball-style dimensional modeling principles - grain, conformed dimensions, fact/ dimension design, star schema - to shape source data into semantic-model-ready structures\nTranslate approved business definitions into governed metrics, relationships, descriptions, and metadata so that AI agents and BI tools consume a single certified definition per KPI.\nFacilitate metric-definition and certification reviews with Analysts, domain owners, and governance stakeholders, documenting calculation logic, grain, filters, assumptions, and ownership.\nBuild and maintain dbt models for the transformation / staging layers that feed each Semantic Model\nOwn the deployment lifecycle for semantic assets: Git-based version control, CI checks, and orchestrated deployment (e.g., via Airflow)\nPartner with Analysts and business stakeholders to define and execute validation scenarios, reconcile Semantic Model outputs against approved sources, test common and edge-case business questions, and confirm that AI-agent and BI results match certified metric definitions.\nDefine and implement automated data quality controls for Semantic Models, including source freshness, uniqueness, referential integrity, accepted values, metric reconciliation, and anomaly detection.\nApply security and governance controls to Semantic Models, including RBAC, least-privilege access, row-level security, column masking, and appropriate handling of confidential or personally identifiable information.\nProduce and maintain clear technical and business-facing documentation for Semantic Models, including grain, entities, relationships, metric definitions, synonyms, example questions, known limitations, ownership, and usage guidance.\nMonitor production Semantic Models for data quality failures, freshness issues, broken dependencies, and changes in upstream Canonical Data assets, and coordinate remediation with the relevant teams.\nTreat each Semantic Model as a product: continuously monitor and evaluate its quality (answer accuracy, query performance, adoption/usage), gather feedback from consumers, and drive iterative improvements over its full lifecycle \nOwn lifecycle management of assigned Semantic Models, from initial release through versioning, deprecation, and retirement of metrics or structures as business needs evolve\nMonitor and help close semantic drift between different consumption paths (e.g., agent-facing vs. BI-facing)\nDefine and continuously refine agent-ready semantic modeling best practices, naming conventions, schema structure, and reusable development patterns as the semantic layer scales to new domains and use cases\nStay current on evolving tooling for semantic layers, including support for AI-agent consumption, and recommend adoption where it improves reliability or scalability\nQualifications / What You Need To Succeed\nBachelor's or Master's in Data Engineering, Computer Science, or a related field\n6+ years of experience in Analytics Engineering, Data Engineering, or BI development\nProven, hands-on experience with semantic modeling for BI - building and maintaining semantic/metrics layers (e.g., LookML, dbt Semantic Layer/MetricFlow, Power BI datasets, Snowflake Semantic Views, or equivalent) with understanding of analytics governance and scalability.\nSolid grounding in Kimball dimensional modeling principles - fact/dimension design, grain, conformed dimensions, star schema, slowly changing dimensions\nStrong SQL and dbt Core experience - models, tests, documentation, and CI-friendly project structure\nWorking knowledge of Git and Git-based development workflows (branching, PRs, code review)\nExperience with orchestration tools (Airflow preferred) for scheduled or triggered deployment pipelines\nHands-on experience with a cloud data platform (Snowflake preferred; Azure/Databricks/BigQuery considered)\nComfort working in a governed, multi-domain environment with defined naming conventions and lifecycle management\nWorking knowledge of data access governance, RBAC, row-level security, column-level masking, and privacy considerations in analytics and AI consumption.\nOptimize Semantic Models and supporting dbt assets for query performance, scalability, maintainability, and warehouse cost, including model materialization, join design, aggregation strategy, and agent-context constraints.\nMaintain clear lineage and dependency documentation across Canonical Data assets, staging models, Semantic Models, certified metrics, BI reports, and AI agents, and perform impact analysis before introducing breaking changes\nExcellent communication and collaboration skills, with the ability to translate business requirements into modeled, tested, documented data assets\nA resilient, persistent, and motivated attitude - able to lead by example and influence through credibility and consistency.\nGood English, written and spoken\nPlus\nExposure to semantic modeling for AI/LLM agents - e.g., Snowflake Cortex Analyst/Agent, text-to-SQL semantic layers, or similar natural-language-to-query interfaces\nFamiliarity with Snowflake-specific semantic tooling (Semantic Views, Cortex Search, Semantic Studio)\nExperience with Power BI (import mode) and awareness of semantic-layer-to-BI drift challenges\nFamiliarity with emerging semantic interoperability standards (e.g., Open Semantic Interchange)\nPython scripting for deployment automation or data quality checks\nMore about OutSystems\nOutSystems is a leading AI Development Platform built for the enterprise. Global organizations trust OutSystems to rapidly build mission-critical apps and agents, modernize legacy processes with agentic systems, and govern their entire AI portfolio across complex regulatory environments, all on one unified platform.\nAs the future becomes agentic, our customers need us now more than ever. While AI has opened the door to extraordinary possibilities, most large organizations find themselves stuck on one side of the \"enterprise gap\" because AI by itself doesn't solve their complex use cases and business challenges. OutSystems bridges the \"enterprise gap\" by combining the speed of generative AI with a deterministic, enterprise-grade framework. We provide the tools for teams of any size to deliver high-quality, reliable AI solutions that drive real business impact.\nWe are looking for passionate, talented, and motivated people to join us as we empower organizations to build, deploy, and scale the next generation of enterprise software. While we are leading the charge into the agentic era, our mission is broader: we are the platform enterprise leaders trust to evolve their entire business, accelerating innovation through secure, governed human-AI collaboration.\nOutSystems is a global company, with more than 900k developer community members, 1,700 employees, more than 600 partners, and thousands of active customers in over 75 countries and across 21 industries. Founded in 2001, OutSystems now has offices in the United States, United Kingdom, the Netherlands, Portugal, Germany, the UAE, Japan, Hong Kong, Malaysia, Australia, India, and Singapore, and includes a thriving, worldwide community of remote employees.\nOur customers are some of the world's most recognizable brands across diverse industries- such as Toyota, Heineken, Bosch, KeyBank, and UCLA-who trust OutSystems to deliver ROI and transformational impact.\nConsistently recognized as a leader by top analyst firms Gartner, IDC and Forrester, OutSystems continues to shape the future of enterprise software development in the agentic era. We are proud to be named a leader in more than 100 categories on G2, including #1 in Customer Satisfaction in Enterprise Low Code Development, and most recently as a leader in AI Agent Building in the G2 Spring 2026 Reports.\nWorking at OutSystems\nOur culture is built on our core values of Trust, Customer Success, Innovation, and Alignment. We operate as one global OutSystems team, taking ownership to pursue our vision of being the AI platform enterprise leaders trust to build, secure, and evolve their most critical applications and systems.\nWhat do we have to offer you?\nA company at the vanguard of the agentic revolution, where we don’t just react to AI innovation-we architect it. Joining OutSystems means stepping onto a high-growth rocket ship that combines the fearless agility of a startup with the sophisticated, global foundation of an enterprise powerhouse.\n\nReal growth opportunities. We don't just talk about development; we invest in it through structured programs designed to scale your expertise. 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