Role Overview:

As an Analytics Manager at Google Operation Center, you will drive measurement and analysis that support marketing programs. You will  utilize insights to enable marketers to develop valuable and highly effective programs. You are primarily responsible for overseeing, managing and evaluating the effectiveness of the workflows operations team.

Position Responsibilities

• Lead and manage a team of data engineers and analysts providing mentorship and technical guidance.
• Design, develop, and maintain scalable data pipelines and ETL processes.
• Optimize data storage and processing solutions for performance, reliability, and cost-effectiveness.
• Implement best practices for data governance, security, and compliance.
• Evaluate and integrate new technologies and tools to enhance data infrastructure.
• Collaborate with marketers, sales experts and other stakeholders to understand data needs and optimize data workflows.
• Assess the feasibility of proposed problem resolutions, including the resource availability and potential schedules; review and approve workarounds and resolutions
• Determining, creating, and implementing internal process improvements, such as redesigning infrastructure for increased scalability, improving data delivery, and automating manual procedures.
• This role also needs to have critical problem solving skills with the data technology related problems and bring innovation to all problems coming their way.

Minimum Qualifications

• 8+ years of experience in data engineering / analytics, with at least 3 years in a leadership role.
• Expert in writing complex SQL queries across various databases (e.g., MySQL, PostgreSQL, Oracle) to extract, transform, and analyze data
• Knowledge of data warehousing concepts (i.e., technical architectures, infrastructure components, estimated time to launch, and reporting/analytics tools and environments).
• Excellent critical thinking and problem solving skills

Preferred Qualifications:

• Experience with real-time data processing and streaming technologies.
• Familiarity with DevOps practices and CI/CD pipelines for data engineering.
• Knowledge of machine learning workflows and MLOps.