Analytics Engineer, Mid-level

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Construct, enhance, and sustain data products and features that aid statistical models and analytical applications, converting raw data into dependable, reusable resources.

• Design and uphold scalable data pipelines (batch and/or streaming), guaranteeing efficiency, reliability, and data quality throughout their lifecycle.

• Engage in analytical data modeling, encompassing bronze, silver, and gold layers.

• Ensure governance, quality, and observability of data products and features, which includes testing, monitoring, SLAs, and alerts.

• Implement and refine processes for transferring data between analytical and production environments, ensuring consistency and traceability.

• Collaborate with Product, Engineering, Infrastructure, Security, and Governance teams, serving as a liaison between technical and business domains.

• Suggest continuous enhancements to data platforms, pipelines, and architecture.

• Generate reports and dashboards as needed, concentrating on facilitating product and technology decisions.

• Ensure clear documentation and standardization of processes, datasets, and features.

⛳️ Requirements

• Strong expertise in SQL and Python for data transformation and analysis.

• Experience in distributed processing (Spark) and platforms like Databricks.

• Proven experience in building and maintaining data pipelines utilizing Airflow or similar orchestration tools.

• Familiarity with version control (Git) and software engineering best practices.

• Understanding of CI/CD principles applied to data pipelines.

• Experience in analytical data modeling, including dimensional modeling, data marts, and bronze/silver/gold layers.

• Proficient in data testing and quality assurance, including validation and monitoring.

• Knowledge of cloud environments and data services, such as AWS, Azure, or their equivalents.

• Ability to articulate ideas clearly to both technical and non-technical audiences.

• Differentials: experience with feature platforms/feature stores; understanding of Data Contracts and modern data governance; familiarity with Kubernetes or distributed architectures; experience in DataOps and/or data observability; and knowledge of statistical modeling and machine learning concepts.

🏝️ Benefits

• Remote work option

• Inclusive environment and a culture that supports balancing career with personal commitments and interests

• Focus on well-being

• Outstanding career and development opportunities

• Recognized by Great Place To Work™ in 24 countries

• International Top Employers certification