Senior Data Engineer

📋 Description

• • Design, build, and maintain scalable and secure ETL/ELT pipelines using Matillion (or similar platforms), Python, and dbt to ingest, transform, and deliver data into Snowflake.
• • Optimize Snowflake data warehouse performance through SQL query tuning, table structure improvements, and warehouse configuration adjustments to ensure efficient data processing and storage.
• • Collaborate with data analysts, data scientists, product managers, and engineering teams to understand data requirements and deliver high-quality, reliable datasets for analytics and operational use cases.
• • Design and maintain scalable dimensional and normalized data models within Snowflake to support both analytical reporting and operational workflows.
• • Implement data quality checks, logging, monitoring, and alerting systems to ensure data accuracy, consistency, and trustworthiness across all pipelines.
• • Maintain comprehensive documentation of data systems, processes, and code, and champion engineering and data governance best practices across the organization.
• • Utilize Airflow (or similar orchestration tools like Prefect or Luigi) to schedule, monitor, and manage complex data workflows and dependencies.
• • Work with cloud platforms such as AWS, GCP, or Azure to deploy and manage data infrastructure components and integrate with external data sources.
• • Apply CI/CD practices and version control (e.g., Git) to automate testing, deployment, and collaboration across data engineering projects.
• • Ensure data pipelines adhere to security and compliance standards while supporting global business operations across 8x8’s international teams.
• • Contribute to the evolution of the modern data stack by evaluating and integrating new tools and methodologies that improve data reliability, speed, and accessibility.
• • Translate business needs into technical data solutions by working closely with cross-functional stakeholders to define data requirements and deliver actionable insights.
• • Participate in code reviews, architecture discussions, and knowledge-sharing sessions to elevate team standards and promote scalable data engineering practices.
• • Support the migration and modernization of legacy data systems into a centralized, cloud-native Snowflake environment with dbt-driven transformation layers.
• • Proactively identify bottlenecks, data quality issues, or inefficiencies in existing pipelines and implement improvements to enhance system performance and maintainability.
• • Ensure all data engineering work aligns with organizational goals for customer experience optimization, operational efficiency, and data-driven decision-making.