Senior Data Engineer / BI

Description

Job Summary: Act as a technical partner to various business areas, managing strategic data requests and ensuring data reliability. Key Highlights: 1. Manage strategic data requests and act as a technical partner. 2. Design, build, and maintain robust and scalable data pipelines. 3. Lead improvements in data architecture and analytical processes. **Your Main Responsibilities:** Receive and manage strategic data requests, acting as a technical partner to various business areas; * Design, build, and maintain robust and scalable (ETL/ELT) data pipelines, ensuring data reliability, traceability, and quality across the entire pipeline; * Model and integrate data from multiple sources (relational databases, APIs, events, files), creating structured data layers (Raw, Trusted, Refined) for analytical consumption; * Develop and maintain data transformations using advanced SQL and programming languages such as Python or Node.js, following best practices for version control and testing; * Create and evolve dimensional data models and reusable business metrics, ensuring a single source of truth; * Build and update dashboards and analytical reports using tools like Metabase, Superset, or similar, with a focus on enabling self-service for consuming teams; * Monitor pipeline health and proactively resolve data incidents (data quality, latency, duplication); * Prepare technical documentation and data-driven reports with diagnostics to support strategic decision-making; * Propose and lead evidence-based improvements to data architecture and analytical processes. **Required Qualifications:** Education: Bachelor's degree in Computer Science, Software Engineering, Information Systems, or related fields; Experience: 2+ years of hands-on experience in data engineering, pipeline development, or data architecture in production environments. **Mandatory Skills:** * Advanced SQL proficiency; * Experience with Python for data manipulation and transformation; * Building and maintaining ETL/ELT pipelines in production environments; * Experience with relational databases (PostgreSQL). **Preferred Skills:** * Experience with pipeline orchestration tools (Airflow, Prefect, or similar); * Knowledge of declarative transformation tools such as dbt; * Experience with data streaming (Kafka); * Familiarity with data lakehouse architectures and formats such as Parquet, Iceberg, or Delta Lake; * Experience with cloud platforms (AWS) and managed data services; * Infrastructure as Code (Terraform, Docker, CI/CD).