{"id":4319864,"url":"https://alion.io/job/yellowipe-data-engineer","title":"Data Engineer","company":{"id":4374582,"name":"YellowIpe","domain":"yellowipe.io","url":"https://alion.io/company/yellowipe","size_band":"51-200","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Apache Iceberg","optional":true},{"name":"AWS Glue","optional":true},{"name":"Databricks","optional":true},{"name":"Feature Store","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-14T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-11-14T00:00:00Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"About YellowIpe\nOur mission is to inspire the connection between technology and people. We nurture the best in our professionals through our expertise in finding and attracting top talent for the best projects. People Focus, Collaboration, and Commitment are the pillars that guide us on this journey.\nJoin the yellow team as our new Data Engineer!\nResponsibilities:\nDesign, build, and operate enterprise-grade ETL and ELT pipelines supporting AI, analytics, reporting, and agentic workflow use cases.\n\nDevelop clean, documented, and reusable data products using operational, customer, logistics, sales, and other enterprise data sources.\n\nImplement data quality checks, monitoring, lineage, metadata, and reliability patterns, ensuring datasets are trustworthy and production-ready.\n\nSupport integrations between Azure-based AI services, Snowflake, APIs, file-based data sources, and AWS data services when required.\n\nCollaborate with business and AI teams to translate use case requirements into effective data models, pipelines, and serving layers.\n\nWork closely with Data Scientists, AI Engineers, Cloud Engineers, business stakeholders, and local IT teams to deliver scalable data solutions.\n\nSupport the onboarding of new data sources while ensuring compliance with data protection, access control, and governance requirements.\n\nContinuously improve the reliability, scalability, and reusability of enterprise data pipelines and data products.\n\nRequirements:\nStrong hands-on experience with Python and SQL.\n\nSolid experience with data modelling and ETL/ELT development, including production-grade data pipelines.\n\nExperience with cloud-native data engineering, ideally within the Azure ecosystem.\n\nExperience with Snowflake or comparable cloud data platforms.\n\nGood understanding of data quality, orchestration, observability, access management, and data governance in enterprise environments.\n\nExperience integrating multiple data sources, APIs, and enterprise systems.\n\nAbility to collaborate effectively with Data Scientists, AI Engineers, business stakeholders, and IT teams in international environments.\n\nFamiliarity with GenAI-assisted software engineering tools such as GitHub Copilot, Cursor, or OpenCode.\n\nNice to have:\nExperience with Apache Iceberg, AWS Glue, Spark, Databricks, or similar data engineering technologies.\n\nPrevious experience working with logistics, parcel operations, customer feedback, route, depot, or sales data.\n\nExposure to AI feature stores, vector databases, retrieval pipelines, or data preparation for Generative AI applications.\n\nImportant information:\nInternational and collaborative work environment.\n\nWorktime flexibility.\n\nCandidatura: Ver a vaga e candidatar-me na YellowIpe","description_format":"text","description_chars":2724,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","IT Outsourcing & 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