Analytics Engineer, Service Ops Analytics & AI

Key Responsibilities

• Data Pipeline Development: Lead the design, development, and deployment of scalable and robust data pipelines, ensuring seamless data integration and processing across diverse systems
• Analytics Engineering Best Practices: Establish and uphold best practices for data engineering, including coding standards, data governance, performance optimization, and automation strategies
• Code Quality and Review: Participate in code reviews, provide constructive feedback, and contribute to the team's continuous improvement in coding practices and methodologies
• ETL/ELT Development: Design, build, and maintain robust ETL/ELT pipelines, reusable frameworks, and libraries to process and transform data from diverse sources, ensuring accuracy, quality, and consistency
• System Monitoring: Proactively monitor and troubleshoot data pipelines, ensuring high availability, reliability, and performance across all data engineering workflows
• Automation and CI/CD: Implement CI/CD pipelines to streamline the deployment, testing, and maintenance of analytics engineering processes
• Cross-functional Collaboration: Partner with data scientists, engineers, analysts, product managers, and business stakeholders to understand requirements, translate them into actionable technical specifications, and deliver impactful data solutions
• Stakeholder Communication: Articulate complex technical concepts to non-technical stakeholders, fostering alignment and ensuring a shared understanding of data initiatives across teams

Qualifications

• Hands-on experience with SQL, Python, dbt, and Snowflake
• Experience in version control systems such as Git, and workflow management tools such as Airflow
• Proven experience in designing and building scalable data pipelines, and architectures
• Strong understanding of data governance, quality assurance, and performance optimization in a data engineering context
• Expertise in ETL/ELT processes, data modeling, and integration of data from multiple sources into a data warehouse
• Experience with CI/CD workflows and tools for data engineering
• Strong problem-solving and analytical skills, with the ability to work effectively in a collaborative environment