Senior Data Engineer Offline

Hello everyone! 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 us. 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?

What exciting things is the product doing for the community?
The product is transforming the way public and private spaces are discovered, reserved, and used across communities – all online and accessible anywhere.

!!! Working hours are 5:00 PM – 2:00 AM Kyiv time, which is 7:00 AM – 4:00 PM Pacific Time (PST/PDT).

What is the team size and structure?
Our team consists of: 1 Tech Lead, 2 Front-End developers, 3 Back-End developers, 1 React-Native developer, 2 UI/UX Designers, 2 QA, 1 AQA, and PM.

How many stages of the interview are there?
Interview with the Recruiter – up to 30 min.;
Technical interview – up to 1 hour;
Interview with the client – up to 1 hour.

Requirements:
Must have:

• 5+ years of experience in Data Engineering or related roles;
• 3+ years of production experience with Snowflake, including data modeling, performance tuning, and cost management;
• 3+ years of experience with dbt, building and maintaining models, including testing, documentation, and incremental models;
• Advanced SQL skills, including complex transformations and query optimization;
• Hands-on experience designing, building, and maintaining data pipelines using both ETL and ELT patterns, from ingestion through transformation;
• Strong experience turning raw, messy data into clean, reliable datasets and designing data models (e.g., dimensional or star schemas) that support analytics and reporting;
• Solid knowledge of Python for building data pipelines, scripting, and automation;
• 2+ years of analytics-focused experience with MongoDB: querying data, getting insights, data validation, and extracting and modeling data for reporting (not application development);
• Proficiency with Git and collaborative workflows, including branching, pull requests, and code reviews;
• Upper-Intermediate English or higher for effective communication with the client and documentation.
   
• It would be a plus:
  Experience with AWS (e.g., Lambda, SQS, S3) in data pipelines;
• Experience with analytics and BI tools such as Sigma, Looker, or similar (e.g., Tableau, Power BI), including building datasets, semantic layers, or dashboards for business users;
• Experience leading data projects or technical initiatives;
• A technical degree (e.g., Computer Science, Engineering, Math).
   
• Soft skills:
  Proactive – you take ownership and act without waiting for direction;
• Confident communicator – you are comfortable leading business and technical calls with the client;
• Detail-oriented – you deliver accurate, high-quality work;
• Initiative-driven – you’re eager to improve processes and take action.
   
• Responsibilities:
  Take ownership of the data side of the project and, over time, lead it;
• Lead business and technical discussions with the client, gather requirements, and make technical decisions;
• Design, build, and maintain reliable ETL/ELT pipelines using Python and dbt, from data ingestion through transformation;
• Model and evolve the data warehouse and data marts in Snowflake to support analytics and reporting;
• Optimize Snowflake performance and manage compute costs;
• Query, validate, extract, and model data from MongoDB into clean, analytics-ready datasets;
• Develop and maintain dbt models (staging, intermediate, and marts) with testing, documentation, and incremental strategies;
• Ensure data consistency and reliability by applying data quality checks, auditing, and reconciliation logic;
• Contribute to the semantic layer and datasets used by BI tools to keep business metrics consistent and accessible;
• Participate in code reviews and follow Git-based collaborative workflows;
• Drive continuous improvement in data engineering processes and data governance standards.