Data Engineer for a Data-Driven eCommerce SaaS

At Your Data Playbook, we believe data is the most valuable asset of the 21st century, and our mission is to unlock that value for eCommerce entrepreneurs worldwide, turning raw data into actionable insights that reveal their biggest growth opportunities. With the AI transformation now underway, that mission is bigger, more exciting and more important than ever. We're looking for a hands-on data engineer to build, monitor and optimize the data pipelines that power the platform, making sure data is reliable, accurate, and delivered to the people who need it. This is a high-ownership role for someone proactive and technically strong who is comfortable working through ambiguity. Strong performers have a clear path to a full-time, long-term seat. What you will do: -Build the pipelines. Create ETL/ELT pipelines from scratch and improve existing workflows. -Keep them healthy. Set up and maintain monitoring and alerting for pipeline health. -Fix the root cause. Run root-cause analysis and resolve issues quickly — durable fixes, not temporary patches. -Move data end to end. Support data ingestion, transformation, and delivery to end users. -Make it scale. Improve reliability and scalability across data workflows. -Communicate clearly. Collaborate cross-functionally and explain technical decisions in plain language. How you work with the team: -High ownership: you own production workflows end to end, not just the ticket in front of you. -You are self-directed — you find your own path through ambiguity and are judged on reliable, accurate, on-time data. -When something breaks, you lead the response: investigate, recover, communicate status, and document the fix. -You leave systems more observable, automated and resilient than you found them. -This s a fast-moving SaaS environment — you balance planned work with real-world interruptions and make pragmatic tradeoffs. Who you are (must have): -Solid background in data engineering or data ops, with real ownership of production workflows. -Strong data modeling and warehouse/lakehouse experience. -Able to own decisions around schema design, storage patterns, and performance tuning — and recommend the right data structure for each workflow based on scalability, reliability, and business needs. -Proven experience building pipelines from scratch (ETL/ELT). -Strong skills in monitoring, alerting and root-cause analysis. -Strong fundamentals in SQL and Python (scripting/automation, debugging data issues). -Clear English communication. Spanish is a plus. -Self-directed, ownership-driven, and comfortable with ambiguity. Strongly preferred (not required): -Alteryx experience. -Cloud platforms, especially AWS (GCP / Azure also fine). -Databricks / Spark concepts, job orchestration, or similar platforms. Nice to have: -BI tools (Tableau / Power BI / Looker), including dataset design for performance. -APIs / webhooks / GraphQL and/or light web scraping for data acquisition. -Docker, Terraform, or other infrastructure tooling. How to apply: -A short, sharp CV with the most relevant data-engineering experience. -Submit a video: Start your message with the word BEYOND so we know you read this. Answer: 1.Why do you think you’re a fit for this role? 2.Which AI tools do you use every day and what’s the most useful thing you have automated with them? 3.What's the most impactful data pipeline you have ever built? *Optional but strong: a 1 to 2 minute Loom video where you just talk to us. We ignore generic applications. Shortlisted people get a small task or/and an interview invitation.