Senior Data Engineer

We 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.

• Design 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)
• Developing 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
• Implement 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
• Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
• Enable 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
• Design scalable logical and physical data models for analytical and operational use cases, ensuring consistency across domains
• Orchestrate 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
• Apply 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)
• Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence, ensuring systems remain trustworthy and production-ready
• Enable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems, including ML and AI platforms
• Continuously monitor and optimize pipelines and infrastructure for performance, scalability, and cost efficiency across our core cloud ecosystems
• Bring genuine curiosity to every engagement — ask the right questions to understand not just what stakeholders are asking for, but why
• Apply common sense thinking, detailed analysis, and experience-based recommendations to help derive and refine business requirements, surfacing gaps or better alternatives where they exist
• Work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions
• Support adoption of data products and contribute to best practices across the data and AI ecosystem

• Strong hands-on experience with Apache Spark and Delta Lake, and strong programming skills in Python and SQL
• Proven experience building batch and streaming data pipelines and production-grade data platforms, with solid understanding of data modeling, data quality, and governance principles
• Track A: Strong, demonstrable hands-on experience with AWS data services (e.g., S3, Glue, EMR, Redshift, Kinesis, Lambda) and the Databricks Lakehouse Platform
• Track B: Strong, demonstrable hands-on experience with Microsoft Azure / Fabric (e.g., Data Factory, Synapse, Fabric Lakehouse, Azure Databricks)
• Experience with lakehouse architectures and distributed data systems, and strong understanding of scalability, reliability, and performance considerations in data pipelines
• Naturally curious, with strong problem-solving skills focused on scalability and reliability, and a collaborative approach to working in cross-functional teams
• Comfortable applying common sense thinking, detailed analysis, and experience-based recommendations to help derive and challenge business requirements rather than taking them at face value
• Currently living in Portugal and have the right to work in Portugal

• Exposure to other environments (Snowflake, GCP) considered a plus
• Experience in Agile or consulting environments is beneficial
• Deep expertise in both tracks — AWS and Azure/Fabric, across Databricks and native Fabric tooling
• Experience with GenAI and AI data systems (e.g., RAG pipelines, vector databases, LLM data preparation)
• CI/CD for data pipelines, and infrastructure-as-code tools such as Terraform or CloudFormation
• Additional exposure to streaming technologies (e.g., Kafka), Spark optimization, or advanced analytics and ML workloads (including causal or experimentation platforms)
• Experience building data products or large-scale analytics platforms

• Competitive compensation package
• Flexibility, with remote and hybrid work options (country-dependent)
• Career advancement, with international mobility and professional development programs
• Learning and development, with access to cutting-edge tools, training and industry experts

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