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)