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We are looking for a Data Engineering Tech Lead to join our data engineering team. In this role, you will be responsible for leading the design, implementation, and evolution of scalable, cloud-based data solutions, while providing strong technical leadership and guidance to the engineering team.If you are passionate about cloud data platforms, solution architecture, and mentoring engineers while delivering high-quality data systems in production, this opportunity is for you. What you will be doing• Lead the design, implementation, and maintenance of scalable and reliable data solutions on AWS (and Azure where applicable).• Act as a technical leader for the data engineering team, providing mentorship, guidance, and hands-on support.• Translate business requirements into technical solutions, break them down into actionable tasks, and distribute work effectively across the team.• Design robust data architectures considering scalability, performance, security, and cost optimization.• Build, optimize, and maintain data pipelines, ETL processes, and orchestration workflows using tools such as Apache Airflow, AWS Step Functions, AWS Glue, and Lambda.• Apply Infrastructure as Code principles using CloudFormation (and Terraform when applicable) to manage and evolve cloud infrastructure.• Work hands-on with Python and SQL to develop, optimize, and troubleshoot data processing workflows.• Design efficient data models and optimize query performance across platforms such as Amazon Redshift, PostgreSQL, and MySQL.• Manage and optimize large-scale datasets, including the use of Iceberg tables for data consistency and performance.• Ensure production readiness through monitoring, logging, and tracing using AWS services such as CloudWatch, CloudTrail, and AWS X-Ray.• Collaborate closely with cross-functional teams, including analytics, product, and business stakeholders.• Produce clear solution designs, technical documentation, and provide training to team members.• Continuously challenge requirements and propose innovative technical solutions to improve efficiency and performance. What we are looking for• Bachelor's or Master's degree in Computer Science, Engineering, or equivalent professional experience.• 4–5 years of experience in data engineering roles, with at least 2–3 years in a Tech Lead or similar technical leadership position.• Strong expertise in AWS data and cloud services, including Lambda, Glue, Step Functions, CloudFormation, and CloudWatch.• Solid experience in solution architecture and designing cloud-native, scalable data systems.• Strong hands-on skills in Python and SQL.• Experience working with relational databases such as PostgreSQL, MySQL, and Amazon Redshift.• Proven experience building and managing ETL pipelines and data integration workflows.• Familiarity with Iceberg tables and managing large-scale datasets.• Strong production awareness, with the ability to proactively monitor, troubleshoot, and resolve issues.• Excellent communication, leadership, and collaboration skills.• Strong analytical and problem-solving mindset, with the ability to challenge and refine business requirements.• Full professional proficiency in English (B2 or higher). Plus (not required but valued)• Exposure to Azure data services and Azure Databricks.• Experience with streaming and real-time data technologies such as Kafka and Apache Flink.• Experience with cloud-native and serverless architectures.• Familiarity with CI/CD practices for data engineering workflows.• AWS and/or Azure cloud certifications. Why join us?• Take a key technical leadership role in building and evolving modern cloud data platforms.• Work in a collaborative, technically strong, and supportive team environment.• Have real influence over architectural decisions and engineering best practices.• Opportunity to mentor engineers and grow as a technical leader.• Continuous learning and professional development opportunities.• A culture that values technical excellence, ownership, and continuous improvement. Location Porto, Portugal – Hybrid model (3 days per week on-site)
