Analytics Engineer

We’re looking for an Analytics Engineer who is passionate about e-commerce, data infrastructure, and automation. In this role, you will be responsible for building and maintaining scalable data pipelines, optimizing our Snowflake data warehouse, and implementing best practices in CI/CD for data and data observability. You will play a key role in ensuring our data infrastructure is reliable, efficient, and supports both internal analytics and customer-facing insights. Your expertise in Python, SQL, and data automation will be crucial in transforming raw data into meaningful insights that drive decision-making across the company.

Key Responsibilities:

Design, build, and maintain scalable and reliable data pipelines and ETL processes;
Implement CI/CD for data workflows, ensuring version control, testing, and automated deployments;
Optimize and manage our Snowflake environment for performance, cost efficiency, and scalability;
Develop and enhance data observability solutions to proactively detect and resolve data issues.;
Support major initiatives, including the migration of embedded analytics, and semantic layer implementation;
Maintain high standards of data quality, governance, and integrity, ensuring reliable analytics for both internal and customer-facing use cases;
Work closely with data analysts, product teams, and engineers to deliver high-impact insights and scalable solutions.

Requirements:

AI first approach.
Strong proficiency in SQL(!) & Python for data engineering tasks.
2+ years of experience in data engineering, analytics engineering, or a related field.
Hands-on experience with Snowflake or other cloud data warehouses.
Familiarity with data automation, orchestration tools, and workflow scheduling (e.g., Airflow, dbt).
Experience implementing CI/CD for data (version control, automated testing, deployment pipelines).
Strong understanding of data modeling best practices and performance optimization techniques.
Excellent communication skills, with the ability to work cross-functionally and translate complex data concepts into actionable business insights.

Advantages:

Familiarity with BI tools (e.g., AWS Quicksight) and building semantic layers for analytics.
Experience with observability tools.
Knowledge of machine learning pipeline deployment for production use cases.
Understand in statistics and experimentation (AB testing)
Passion for eCommerce and experience in a fast-paced startup environment.
Domain knowledge in logistics, web analytics, ecommerce.