Data Engineer

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Requirements:

• Bachelor's degree in data engineering, big data analytics, computer engineering, or a related field.
• 5 years of proven experience as a data engineer, software developer, or in a similar role.
• Proficiency in Python, C++, Java, R, and SQL.
• Familiarity with Hadoop or equivalent technologies.
• Strong analytical and problem-solving skills.
• Ability to work independently and collaboratively.
• Detail-oriented and conscientious approach to tasks.
• Capacity to manage a pipeline of responsibilities with minimal supervision.

Responsibilities:

• Develop and maintain efficient data pipeline architecture.
• Construct intricate data sets that align with functional and non-functional business requirements.
• Identify, plan, and implement internal process enhancements, such as automation of manual processes, data delivery optimization, and infrastructure redesign for improved scalability.
• Establish the necessary infrastructure for efficient data extraction, transformation, and loading from diverse data sources using SQL and AWS 'big data' technologies.
• Design analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other crucial business performance metrics.
• Collaborate with stakeholders including Executives, Product Managers, Data and Design teams to address data-related technical challenges and fulfill data infrastructure needs.
• Maintain data security and separation across national boundaries, encompassing multiple data centers and AWS regions.
• Develop data tools for the analytics and data science teams, assisting them in optimizing our product's position as an innovative industry leader.
• Collaborate with data and analytics experts to enhance functionality within our data systems.
• Communicate with clients to elucidate task requirements.
• Conceptualize and create infrastructure to facilitate access and analysis of big data.
• Refine existing frameworks to enhance their functionality.
• Test and validate structures to ensure their suitability.
• Prepare raw data for manipulation by data scientists.
• Detect and rectify errors in work.
• Ensure that work remains backed up and accessible.
• Stay current with industry standards and technological advancements that enhance the quality of outputs.