5+ years of experience in data engineering or related fields.
Strong knowledge of API integrations and data pipeline architecture.
Experience with data quality monitoring processes.
Proficient in writing unit and integration tests for data processes.
Familiarity with data modeling and SQL languages, particularly HiveSQL and SparkSQL.
Responsibilities
Develop scalable data pipelines and API integrations.
Collaborate with analytics and business teams to enhance data models.
Implement monitoring processes to ensure data accuracy and availability.
Write tests, document processes, and contribute to engineering knowledge bases.
Analyze data to troubleshoot and resolve issues.
Work with frontend and backend teams on various engineering tasks.
Design data frameworks and evaluate tools for data lineage.
Benefits
Flexible working arrangements to support work-life balance.
Comprehensive health insurance and wellness programs.
Opportunities for professional development and training.
Collaborative work environment that encourages innovation.
Access to cutting-edge tools and technologies.
Full Job Description
Develops and maintains scalable data pipelines and builds out new API integrations to support continuing increases in data volume and complexity.
Collaborates with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility and fostering data-driven decision making across the organization.
Implements processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
Writes unit/integration tests, contributes to engineering wiki, and documents work.
Performs data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
Works closely with a team of frontend and backend engineers, product managers, and analysts.
Defines company data assets (data models), spark, sparkSQL, and hiveSQL jobs to populate data models.
Designs data integrations and data quality framework.
Designs and evaluates open source and vendor tools for data lineage.
Works closely with all business units and engineering teams to develop strategy for long term data platform architecture.
