Analytics Engineer

About Us:

PrettyDamnQuick is a dynamic and innovative SaaS B2B startup transforming online checkout and delivery operations for leading e-commerce brands. Our mission is to enable an Amazon-like shipping experience for any brand, empowering e-commerce companies to optimize their checkout pages through robust A/B testing, analytics, and actionable insights. By turning data into increased revenue and profit, we're not just enhancing the online shopping experience; we're setting a new standard for it. As a small but rapidly growing team, we value creativity, collaboration, and a drive for excellence. With our sights set on scaling from hundreds to thousands of clients, we're looking for innovative minds to join us on our journey to redefine e-commerce analytics.

The Role:

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.

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:

Must Have:

• 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.

Nice to Have:

• 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.

How to Apply:

To apply, please click apply and upload your resume and a brief cover letter outlining your experience in Python, SQL, data pipelines, and CI/CD for data workflows. Feel free to include any relevant projects or work samples that showcase your expertise in data engineering and infrastructure. Share why you're passionate about e-commerce and how you envision contributing to our mission.