{"id":3728526,"url":"https://alion.io/job/valtech-senior-data-engineer-12","title":"Senior Data Engineer","company":{"id":8990,"name":"Valtech","domain":"valtech.com","url":"https://alion.io/company/valtech","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["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":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"Agile","optional":true},{"name":"Apache Kafka","optional":true},{"name":"CI/CD","optional":true},{"name":"LLM","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-15T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-10-15T00:00:00Z","board_verified":false,"closed_at":null,"days_open":22,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":22},"description":"Sobre a vaga\nWe are looking for an experienced Senior Data Engineer to design, build, and optimize modern, cloud-based data platforms that power analytics, AI, and data products across the organization. You will work on scalable batch, streaming, and near-real-time pipelines, enabling high-quality, curated datasets while ensuring robust data governance, security, and observability.\n\nO que vais fazer\n\nDesign and implement scalable data platforms and pipelines on whichever of our two core ecosystems matches your strengths — AWS (with Databricks) or Azure/Fabric (with Databricks or native Fabric tooling)\nDeveloping reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake, and building ingestion, transformation, and curation workflows for both structured and unstructured data\nImplement modern data architectures including lakehouse patterns and medallion layering (bronze, silver, gold) within Databricks or Fabric, ensuring systems are reusable, scalable, and aligned with enterprise needs\nDeliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems\nEnable data pipelines for GenAI use cases (including LLMs, RAG pipelines, and vector-based data flows), as well as agent-based architectures and intelligent workflows, ensuring that data is model-ready and production-grade\nDesign scalable logical and physical data models for analytical and operational use cases, ensuring consistency across domains\nOrchestrate workflows using tools such as Airflow, dbt, Databricks Workflows, Azure Data Factory, or equivalents, with strong focus on automation, reliability, and maintainability of end-to-end pipelines\nApply modern architecture patterns including event-driven and streaming architectures, and ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC)\nEstablish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence, ensuring systems remain trustworthy and production-ready\nEnable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems, including ML and AI platforms\nContinuously monitor and optimize pipelines and infrastructure for performance, scalability, and cost efficiency across our core cloud ecosystems\nBring genuine curiosity to every engagement — ask the right questions to understand not just what stakeholders are asking for, but why\nApply common sense thinking, detailed analysis, and experience-based recommendations to help derive and refine business requirements, surfacing gaps or better alternatives where they exist\nWork closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions\nSupport adoption of data products and contribute to best practices across the data and AI ecosystem\n\nO que é pedido\n\nStrong hands-on experience with Apache Spark and Delta Lake, and strong programming skills in Python and SQL\nProven experience building batch and streaming data pipelines and production-grade data platforms, with solid understanding of data modeling, data quality, and governance principles\nTrack A: Strong, demonstrable hands-on experience with AWS data services (e.g., S3, Glue, EMR, Redshift, Kinesis, Lambda) and the Databricks Lakehouse Platform\nTrack B: Strong, demonstrable hands-on experience with Microsoft Azure / Fabric (e.g., Data Factory, Synapse, Fabric Lakehouse, Azure Databricks)\nExperience with lakehouse architectures and distributed data systems, and strong understanding of scalability, reliability, and performance considerations in data pipelines\nNaturally curious, with strong problem-solving skills focused on scalability and reliability, and a collaborative approach to working in cross-functional teams\nComfortable applying common sense thinking, detailed analysis, and experience-based recommendations to help derive and challenge business requirements rather than taking them at face value\nCurrently living in Portugal and have the right to work in Portugal\n\nValorizado\n\nExposure to other environments (Snowflake, GCP) considered a plus\nExperience in Agile or consulting environments is beneficial\nDeep expertise in both tracks — AWS and Azure/Fabric, across Databricks and native Fabric tooling\nExperience with GenAI and AI data systems (e.g., RAG pipelines, vector databases, LLM data preparation)\nCI/CD for data pipelines, and infrastructure-as-code tools such as Terraform or CloudFormation\nAdditional exposure to streaming technologies (e.g., Kafka), Spark optimization, or advanced analytics and ML workloads (including causal or experimentation platforms)\nExperience building data products or large-scale analytics platforms\n\nO que é oferecido\n\nCompetitive compensation package\nFlexibility, with remote and hybrid work options (country-dependent)\nCareer advancement, with international mobility and professional development programs\nLearning and development, with access to cutting-edge tools, training and industry experts\n\nResumo organizado por IA a partir do anúncio original. 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