Senior Data Engineer

We are looking for a Senior Data Engineer to join a project in the tech capital of the world – Silicon Valley. 17:00–02:00 Kyiv time = 7:00–16:00 Pacific Time (PST/PDT). Project Idea The project was founded back in 2014 with the goal of connecting private and government universities with regular people like we are. You have a variety of auditoriums, gyms, classrooms, and other venue options available for community use – schedule facility uses and manage requests from the community all in one place. Just imagine that you’re a football player and you can rent a football field at Harvard to play with your friends. Amazing, right? Must have: 5 + years of experience in Data Engineering or related roles; Strong SQL skills and hands-on experience with PostgreSQL (including Aurora Serverless); Solid knowledge of Python for data processing and automation; Experience building and maintaining ETL/ELT pipelines using cloud-native tools (e.g., AWS Lambda, S3, SQS); Proven experience working with MongoDB and MongoDB Atlas, including event-driven architectures using Atlas Triggers, Stream Processing; Proficiency with dbt for building modular, testable, and well-documented data transformation workflows; Good understanding of data modeling principles for OLAP/OLTP systems, including normalization and dimensional modeling; Demonstrated experience designing and implementing data warehouses and data marts; Working knowledge of Node.js, particularly in backend logic tied to data ingestion or transformation workflows; Familiarity with cloud data platforms (e.g., AWS) and serverless computing patterns; A technical degree (e.g., Computer Science, Engineering, Math) is a plus; Upper-Intermediate English or higher for effective communication and documentation. Soft skills: Proactive – you take ownership and act without waiting for direction; Detail-oriented – you deliver accurate, high-quality work; Initiative-driven – you’re eager to improve processes and take action. Responsibilities: Design, implement, and maintain scalable and reliable data pipelines using Python, dbt, and AWS Lambda; Build and optimize data architectures to support analytics, reporting, and machine learning use cases, including data warehouse and data mart modeling on PostgreSQL (Aurora Serverless); Develop and manage ELT workflows that extract data from MongoDB (using Atlas triggers) and load into staging and production layers in PostgreSQL; Ensure data consistency and lineage by applying robust data quality checks, auditing, and reconciliation logic; Collaborate with cross-functional teams to gather data requirements, understand business logic, and translate them into efficient data models and transformations; Monitor and troubleshoot SQS/Lambda-based ingestion pipelines, addressing issues related to concurrency, message processing, and data duplication; Contribute to the semantic layer design used by BI and reporting tools to ensure consistency and accessibility of business metrics; Maintain and evolve dbt models (staging, intermediate, and marts) aligned with software engineering and analytics best practices; Drive continuous improvement in data engineering processes and data governance standards, ensuring scalability, maintainability, and security.