Design, develop and implement robust data pipelines, connecting data from multiple sources using primarily tools from the AWS ecosystem;
Understand business requirements and those of various software products and translate them into efficient, scalable data products;
Serve as a technical reference for the architectural design of data-driven (Data-Driven) and AI-first (AI-First) solutions, coordinating between product, analytics and ML to define standards, best practices, SLAs, observability and platform governance;
Implement scalable and efficient data enrichment solutions using DBT to transform raw data into tailored data models and actionable business insights;
Manage integrations, transformation processes and task automations using data connectors, SQL APIs, Python and REST;
Use SQL to query and manipulate data in relational and non-relational databases;
Apply Spark for processing large volumes of data in real time (Kafka and similar) and in batch.

AWS (Athena, S3, EMR, Glue, Lambda);
Proficiency in SQL, DBT, Python and Spark;
Experience with data orchestration tools such as Apache Airflow;
Knowledge of different data architecture and modeling techniques for analytics purposes (e.g., Data Mesh, Data Mart, Data Lake, Data Warehouse);
Experience with Data Modeling (relational and/or analytical) and ETL processes;
Experience in Software Development (APIs, gateways, microservices and containers) and CI/CD;
Understanding of software engineering best practices (documentation, automated testing, clean code, monitoring (Datadog)).

Senior Data Engineer

Key skills