Data Engineer, Reach Security

• 3+ years of experience in data engineering with a specific focus on building and managing data pipelines
• Strong proficiency in Python and experience with Apache Airflow or Dagster
• Expertise in developing solutions within Data Warehouse, Data Lake, and Lakehouse architectures
• Deep understanding of ETL/ELT processes, data transformation techniques, and workflow orchestration
• Experience working with cloud-based data platforms and services (AWS, Azure, GCP, etc.)
• Solid foundation in data modeling, schema design, and optimization techniques
• Excellent problem-solving skills, capable of addressing challenges around data consistency, performance, and scalability
• Strong communication skills with the ability to articulate complex data engineering concepts clearly
• A proactive and collaborative mindset, comfortable working independently and within fast-paced teams
• Must be a US citizen or Green Card holder

Desirable

• We are seeking Data Engineers at all levels to design, build, and manage robust data pipelines to support analytics use cases within a Lakehouse architecture
• You will play a critical role in developing scalable solutions using Apache Airflow or Dagster to ingest, transform, and manage large volumes of data efficiently and reliably for analytic workloads
• The ideal candidate is a motivated problem solver who prioritizes high-quality solutions and excels in navigating ambiguity
• As an early team member, you will have the opportunity to take ownership of various aspects of our backend from day one
• Your role will be pivotal in establishing engineering best practices, balancing engineering priorities with business needs, and identifying innovative approaches to deliver outstanding value to our users
• Your engineering knowledge will be applied by designing top-notch architectures, offering insightful feedback on technical designs, solving difficult problems and conducting thorough code reviews, all aimed at ensuring the software we build is both maintainable and dependable
• Design, implement, and maintain scalable and reliable data pipelines using Apache Airflow or Dagster
• Work closely with Platform and Product teams to ensure efficient data ingestion, transformation, and storage strategies
• Develop and optimize data models and schemas that power analytical queries and reporting
• Ensure data integrity, quality, and consistency across Data Warehouse, Data Lake, and Lakehouse environments
• Troubleshoot and optimize performance bottlenecks in complex data processing workflows
• Collaborate in defining engineering best practices, standards, and processes to enhance team productivity and quality
• Proactively identify opportunities to enhance pipeline efficiency, scalability, and reliability