Senior Analytics Engineer
outsystems · Portugal, Lisbon
There are NO limits to your career: come shape the future and be part of a truly unique global culture at OutSystems!
About This RoleAs 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.
You'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..
As 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.
Role Context
This 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.
You'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.
You'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.
You'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.
What You Will Lead/Do
- Design and build Semantic Models on top of the Canonical Data Model, covering existing and future business domains as the semantic layer scales
- Apply Kimball-style dimensional modeling principles — grain, conformed dimensions, fact/ dimension design, star schema — to shape source data into semantic-model-ready structures
- Translate approved business definitions into governed metrics, relationships, descriptions, and metadata so that AI agents and BI tools consume a single certified definition per KPI.
- Facilitate metric-definition and certification reviews with Analysts, domain owners, and governance stakeholders, documenting calculation logic, grain, filters, assumptions, and ownership.
- Build and maintain dbt models for the transformation / staging layers that feed each Semantic Model
- Own the deployment lifecycle for semantic assets: Git-based version control, CI checks, and orchestrated deployment (e.g., via Airflow)
- Partner 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.
- Define and implement automated data quality controls for Semantic Models, including source freshness, uniqueness, referential integrity, accepted values, metric reconciliation, and anomaly detection.
- Apply 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.
- Produce 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.
- Monitor 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.
- Treat 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
- Own lifecycle management of assigned Semantic Models, from initial release through versioning, deprecation, and retirement of metrics or structures as business needs evolve
- Monitor and help close semantic drift between different consumption paths (e.g., agent-facing vs. BI-facing)
- Define 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
- Stay current on evolving tooling for semantic layers, including support for AI-agent consumption, and recommend adoption where it improves reliability or scalability
Qualifications / What You Need To Succeed
- Bachelor's or Master's in Data Engineering, Computer Science, or a related field
- 6+ years of experience in Analytics Engineering, Data Engineering, or BI development
- Proven, 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.
- Solid grounding in Kimball dimensional modeling principles — fact/dimension design, grain, conformed dimensions, star schema, slowly changing dimensions
- Strong SQL and dbt Core experience — models, tests, documentation, and CI-friendly project structure
- Working knowledge of Git and Git-based development workflows (branching, PRs, code review)
- Experience with orchestration tools (Airflow preferred) for scheduled or triggered deployment pipelines
- Hands-on experience with a cloud data platform (Snowflake preferred; Azure/Databricks/BigQuery considered)
- Comfort working in a governed, multi-domain environment with defined naming conventions and lifecycle management
- Working knowledge of data access governance, RBAC, row-level security, column-level masking, and privacy considerations in analytics and AI consumption.
- Optimize 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.
- Maintain 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
- Excellent communication and collaboration skills, with the ability to translate