Senior Data Engineer, TheGuarantors

• Bachelor's degree in Computer Science, Information Technology, or a related field
• We are seeking a candidate with at least 5 years of experience in data engineering, with expertise in Python, SQL, and cloud platforms
• 5+ years of applicable experience with data engineering concepts and practice, over the full data lifecycle
• Demonstrated proficiency with SQL and Python in data engineering workflows
• Proficiency in utilizing and managing data integration and ETL tools (e.g., Stitch/Talend, Airbyte, Fivetran, etc)
• Experience with data orchestration and workflow management tools (e.g., Dagster, Airflow)
• Applied knowledge of data warehousing concepts and technologies (e.g., Snowflake, AWS Redshift, Google BigQuery, etc)
• Experience in deployment of various data pipelines and components and ML-ops solutions within cloud computing platforms, particularly in AWS
• Excellent problem-solving skills and attention to detail
• Strong communication and teamwork skills, with a demonstrated competency for collaboration and mentoring
• Ability to work in a fast-paced, collaborative environment
• Aptitude for continuous improvement through learning and sharing, along with an ability to quickly and effectively adopt and adapt to new technologies and tools

Desirable

• As a Senior Data Engineer at TheGuarantors, you will design, build, and maintain scalable data pipelines while ensuring data reliability, accuracy, and integrity
• Your responsibilities will include leading data ingestion, transformation, and warehousing initiatives to support business objectives
• In this role, you will collaborate closely with the data engineering team, mentor colleagues, and work alongside business stakeholders to deliver impactful data solutions
• Data Ingestion and ETL/ELT: Design and implement data pipelines to collect, ingest, and transform data from various sources into a structured format
• Data Quality: Collaborate with the team to monitor and maintain data quality standards, perform data validation, and resolve data quality issues
• Data Warehousing: Design and maintain data warehousing solutions to store and manage large volumes of data efficiently
• Automation and Orchestration: Work on automating routine data engineering tasks to improve efficiency and reliability
• Data Apps: Develop and maintain lightweight data applications for ML interfaces, simple CRUD operations, and quick interactive analysis
• Performance Optimization: Identify and implement optimizations to all parts of the data stack, to improve data pipeline performance and reduce processing times
• Documentation: Create and maintain documentation for data pipelines, processes, and data flows
• Collaboration: Collaborate with cross-functional teams including data analysts, data scientists, other engineering teams, and business stakeholders to understand data requirements and deliver data solutions
• Continuous Learning: Stay up to date with industry best practices and emerging technologies in data engineering
• Team Mentorship: Serve as an informal leader on the data engineering team, collaborating with other data engineers on solution designs, and help to continually raise the collective competency of the team by sharing learnings and mentoring team members in earlier stages of their careers
• Project Ownership: Interact effectively with leadership, team members, other engineering / technical teams, and business stakeholders to drive projects to effective and timely resolutions