Job VC
Senior Data Engineer
Technologies
Description
We are looking for a Senior Data Engineer to join a project in the tech capital of the world – Silicon Valley.
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:
4+ 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.
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:
4+ 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.