Analytics Engineer

📋 Description

• • Implement and maintain data pipelines for analytics and operational workflows using SQL and Python, ensuring reliability and scalability across global data sources.
• • Design, develop, and document data warehouse models, tables, and schemas using modern data modeling techniques to support efficient and maintainable analytics solutions.
• • Build and sustain data orchestration workflows using Airflow (Astronomer) to automate and monitor data processing tasks across multiple environments.
• • Apply dbt (data build tool) best practices to transform raw data into analytical datasets, including modular SQL models, testing, and documentation.
• • Collaborate with engineering, analytics, and business teams to identify data needs and deliver aligned solutions that support decision-making and operational workflows.
• • Enforce data governance standards and handle PII and sensitive data securely within cloud environments, ensuring compliance with privacy regulations.
• • Manage and optimize data infrastructure on AWS, leveraging cloud services to support scalable data storage, processing, and access.
• • Participate in code reviews, knowledge sharing sessions, and documentation efforts to enhance team proficiency and maintain high-quality data engineering practices.
• • Contribute to the continuous improvement of data engineering processes, including performance tuning, error handling, and monitoring of data pipelines.
• • Work with modern data stack components including Fivetran, Snowflake, Monte Carlo, and Atlan to enable end-to-end data flow, quality assurance, and lineage tracking.
• • Maintain and optimize CI/CD pipelines to automate testing, deployment, and version control of data artifacts and infrastructure changes.
• • Focus on data quality by implementing monitoring, validation, and alerting mechanisms to detect and resolve data anomalies proactively.
• • Support cross-functional teams in understanding data models, schemas, and access patterns to ensure consistent and accurate usage of analytics assets.
• • Adapt workflows to accommodate global data compliance requirements across 185+ countries, aligning technical implementations with regional legal and operational constraints.
• • Engage in peer collaboration across time zones to ensure seamless integration of data solutions in a distributed, global team environment.