
Senior Data Engineer
About Gigs
At Gigs, we're building the operating system for mobile services—a platform that lets tech companies embed global connectivity into their products effortlessly.
Just as Stripe lets any business add a payment button in seconds, Gigs empowers platforms to weave in connectivity—bridging the traditional world of telecom with modern tech. From fintechs launching mobile services to HR platforms offering work phone plans, we automate provisioning and remove telecom complexity.
Our team of around 100 people across the US and Europe is backed by nearly $100 million in funding from Ribbit Capital, Google, and Y Combinator. We’re tackling deep technical and regulatory challenges to make connectivity truly seamless.
If you’re driven by curiosity, creativity, and the chance to shape the future of telecom, we’d love to hear from you.
Things We Care About
Speed: We move and we ship. We set bold deadlines and treat every week like it matters.
Ownership: If you see something broken, fix it. We don't wait for permission.
Customer Obsession: Our customers' product is our product.
Ambiguity: We're building frontier technology in a complex domain. You'll need sound judgment and good instincts to make decisions without complete information.
First Principles: We don’t ask how things have been done before. We ask why they were done that way at all.
The Role
As a Senior Data Engineer, you'll own the technical roadmap for scaling our data platform. You’ll evolve 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, 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 the fundamentals—600+ dbt models, customer data exports, and an emerging Insights API. Now we need to productionize data sharing, improve loading and pre-processing, and scale with growing data volumes.
What You Will Do
Shape the data platform roadmap by working with stakeholders across Data, Engineering, Finance, Product, Ops, Security, and Growth
Design and implement a generalized, self-service data sharing framework for customers
Extend the orchestration layer (Dagster) to improve reliability, observability, and scalability
Own data infrastructure as code with Terraform, extending and maintaining GCP resources (BigQuery, GCS, Cloud Run, GKE)
Build customer-facing data products, scaling the Insights API and exposing metrics, reports, and dashboards
Maintain and improve data pipelines, moving heavy event processing out of dbt jobs and sourcing new data points
Architect for future ML workloads, ensuring data architecture is “AI-ready” for churn prediction and plan optimization
What We Are Looking For
7+ years in data engineering with architectural leadership experience
Expert Python skills and strong software engineering standards
Strong opinion on data modeling (dbt/SQL) and ability to organize scalable projects
Cloud-native fluency with GCP (BigQuery, Cloud Run, GKE) or AWS equivalents
Infrastructure-as-Code experience with Terraform and DevOps best practices
Product-minded engineering approach, defining SLAs and treating analytics/APIs as live products
A “force multiplier” mindset—elevating the team through reviews, tooling, and simplifying complexity
Work at Gigs
At Gigs, we value in-person collaboration. We believe the best ideas, decisions, and relationships are built when teams spend meaningful time together.
Expect regular time in one of our hubs, plus occasional travel for workshops, customer meetings, and Gigs Republic, our bi-annual company offsite.
Offices are designed to feel like home-inspired workspaces, with plants, thoughtful tools, and small, tight-knit teams that make collaboration natural and energizing.
What We Offer
Competitive compensation and stock options
Stipends for home office or work setup
Budget for learning and development to fuel your career
Free phone and international data plan
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