The Role As a Senior Data Engineer, you'll own the technical roadmap for scaling our data platform. You will be instrumental in evolving our stack from its solid foundation to a production-grade system that powers both internal analytics and customer-facing data products. You'll join a team of 3 across data engineering and analytics and will be working at the intersection of engineering, product, and business. Our data platform is built on modern tooling, BigQuery, dbt, Dagster, Fivetran, and Lightdash. We've proven out the fundamentals: 600 dbt models, customer data exports, and an emerging Insights API with Lightdash and Dagster self-hosted. We need to productionize data sharing, improve loading and pre-processing and enable us to scale with growing data volumes. This is a role for someone who has built data platforms before, knows what "production-ready" means and wants to build on top of a solid foundation and focus on reliability, observability and scale. What You Will Do Shape the data platform roadmap: Work closely with stakeholders across Data, Engineering, Finance, Product, Ops, Security and Growth to understand their data needs and translate them into platform capabilities. You'll help define what "great" looks like for data at Gigs. Productionise data sharing: Today we share data with customers via bespoke pipelines and Hightouch (S3, SFTP, encrypted exports). You'll design and implement a generalised, self-service data sharing framework that can onboard new customers without custom code. Extend the orchestration layer: Our Dagster deployment handles data transformations, data delivery, and operational jobs. You'll improve reliability (retries, alerting), add observability, and ensure our pipelines scale gracefully as data volumes grow. Own data infrastructure as code: All our infrastructure lives in Terraform. You'll extend and maintain GCP resources (BigQuery, GCS, Cloud Run, GKE) while keeping accessibility, security and cost efficiency front of mind. Build customer-facing data products: Scale our Insights API and design the infrastructure to expose metrics, reports, and dashboards directly within Gigs customer products. You'll partner with Product and Engineering to define data contracts and ensure low-latency, high-reliability delivery. Maintain and improve data pipelines: Move heavy event processing out of our dbt jobs, ingest new events in collaboration with engineering teams and figure out how to source data points we currently don’t have access to. Architect for future ML workloads: We don't run models in production today, but we know we will. You’ll ensure our data architecture (granularity, storage patterns) becomes "AI-ready," paving the way for future churn prediction and plan optimization. What We Are Looking For 7 years in data engineering with a track record of architectural leadership. You haven't just built pipelines; you've designed data platforms. You know how to evolve a system from "startup scrappy" to "enterprise reliable" without over-engineering. Expert Python & Software Engineering standards. You treat data infrastructure as software. You are comfortable writing custom Python extractors, implementing CI/CD for data, and you know why unit testing a pipeline is non-negotiable in a production environment. Strong opinion on Data Modeling (dbt/SQL). You know your way around complex SQL, but more importantly, you know how to organize a dbt project so it doesn't collapse under its own weight as the team grows. Cloud-Native & IaC Fluency. You have deep experience with GCP (BigQuery, Cloud Run, GKE) or AWS equivalents. You are comfortable defining infrastructure in Terraform and can mentor the team on DevOps best practices. Product-Minded Engineering. You care about why the data is being moved, not just how. You have experience defining SLAs with stakeholders and treat internal analytics and external APIs as live products that require monitoring and stability. Force Multiplier. You enjoy elevating the team around you. Whether it’s through code reviews, introducing better tooling, or simplifying complex requirements, the team moves faster because you are there.