sr. data engineer
4 weeks ago
Responsible for designing and establishing the solution architecture for analytical platforms and/or data-enabled solutions. Broad understanding of the entire data and analytics eco-system of tools and technologies, with hands-on experience across all three core data domains of data science, data engineering, and data visualization. Responsible for fleshing out the details of the end-to-end component architecture being implemented by the team. Contributes to the team's development velocity, if necessary. Understands how an organization's data platform integrates within the overall technical architecture. Understands the business purpose the role fulfills, and the roadmap for the lifecycle of an organization's systems. Sets the direction and establishes the approach for integrating information applications and programs.
Job Responsibilities
Develop and maintain data pipelines for efficient extraction, transformation, and loading (ETL) processes, including Amazon Redshift data warehouses and Amazon S3 data lakes. Ensure data quality and integrity through validation, cleansing, and error handling processes. Collaborate with data analysts, data architects, and software engineers to understand data requirements. Implement data security measures and access controls to protect sensitive information. Automate and improve data processes and workflows for scalability and efficiency. Monitor data infrastructure performance and address any issues promptly. Stay updated on industry trends and emerging technologies in data engineering. Document data pipelines, processes, and best practices to facilitate knowledge sharing. Continuously optimize data architecture to support the company's evolving data needs. Work cross-functionally to drive data-driven decision-making within the organization.
Requirements
- Bachelor's degree in Computer Science, Information Technology
- Minimum 6 years of experience in data engineering, with knowledge of AWS services, data storage and management, data processing and ETL, data warehousing, programming and scripting, data modeling and architecture, monitoring and optimization, and SQL proficiency. Proficiency in languages like Python, Java, or Scala for data manipulation and automation. Strong data modeling skills.
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