Sr Data Analyst

Responsibilities

• Developes, maintains, and tests infrastructures for data generation.
• Creates and maintains optimal data pipeline archictecture and assembles data to meet business needs.
• Assembles large, complex datasets that meet functional / nonfunctional business requirements.
• Identifies, designs, and implements internal process improvements; automates manual processesprocesses, optimizing data delivery and redesigning infrastructure for greater scalability.
• Builds infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and big data technologies.
• Works with stakeholders including the Executive, Product, Data, and Design teams to assist with datarelated technical issues and support data infrastructure needs.
• Keeps data separated and secure across national boundaries through multiples data centers and AWS regions.
• Creates data tools for analytics and data scientist team members that that assist with building and optimizing products into innovative, industry leaders.
• Oversees data generation infrastructure.
• Builds analytics tools to provide actionable insights and assist data science and analytics team members with building bestinclass products.
• Work with stakeholders to assist with datarelated tech issues and support data infrastructure needs.
• Lead: Recognized subject matter expert in sub-function typically obtained through advanced education and work experience.
• Responsibilities typically include: Managing large projects or processes and/or products and systems with limited oversight from management.
• Coaching, reviewing and delegating work to lower-level professionals.
• Facing IT function related problems that are difficult and moderately complex.
• Serving as an expert within own discipline.

Qualifications

• University Degree and minimum 7 years of prior relevant experience, or equivalent experience.
• Preferred: SQL Python, Azure data factory, Data Bricks, Spark (experience would be a plus)
• Data transformation
• Cloud Data Experience
• Distributed data warehouse knowledge
• Data Lakes
• Communication and stakeholder management skills

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