followed by Seasoned, the leading platform for restaurant hiring. Each acquisition has brought JobGet closer to the frontline, making the platform faster, smarter, and more effective.
We serve a diverse range of industries, from food service and customer support to healthcare, logistics, and manufacturing. With access to over 200 million job seekers, our platform leverages cutting-edge AI to match qualified, role-ready candidates, automating key parts of the hiring process for faster decisions and better outcomes. Teamwork - We make time to help colleagues across JobGet succeed.
Innovation - We're a team of creative, outside-the-box thinkers who thrive in a continuously evolving environment. To us, innovation is about questioning the status quo and always striving to do things better.
It requires self-discipline and the drive to contribute to something greater than ourselves.
As our Principal Data Engineer, you will help lead the technical direction for how data flows, scales, and powers every decision at JobGet. You'll own the data architecture behind our platform that connects hundreds of millions of job seekers with employers across dozens of industries; and you'll do it at the intersection of high-throughput pipelines, real-time matching signals, and AI-driven product experiences.
Data Platform Architecture & Strategy
Champion Modern Data Stack Adoption: Evaluate and introduce best-in-class tooling (Databricks, Snowflake, dbt, Kafka, etc.) aligned with engineering principles and business needs.
you are empowered to make significant technical decisions and drive projects end-to-end with limited oversight.
Data Modeling & Pipeline Engineering
Design and implement robust batch data pipelines using Snowflake and dbt as the core of JobGet's transformation layer,



ingesting and serving data across product telemetry, marketplace signals, employer activity, and candidate behavior.
Lead Data Modeling Decisions: Establish data modeling standards (schema design, dimensional modeling, and data contracts) that serve analytics, ML feature engineering, and product instrumentation.
Personally take on the most complex data integration, performance, and reliability challenges that require principal-level judgment and hands‐on implementation.
Architect and evolve streaming data infrastructure using technologies such as KSQL and Apache Flink to power real-time matching, recommendation, and product decisioning systems.
Enable Machine Learning at Scale: Partner closely with Platform Engineers and Data Analysts to design feature stores, training data pipelines, and inference-ready datasets that power JobGet's AI matching engine.
Accelerate Analytics Delivery: Work with Data Analytics stakeholders to ensure data products are well‐modeled, documented, and performant for self‐serve consumption. Data Governance, Quality & Security
Establish Data Governance Standards: Define and enforce policies for data quality, lineage, access control, and cataloging that scale across a growing and multi-product organization.
Protect Data Integrity: Implement data validation frameworks, SLA monitoring, and anomaly detection to maintain trustworthy data across all consumers.




Design data systems with privacy‐by‐default principles, ensuring alignment with applicable regulations (CCPA, GDPR, etc.) across candidate and employer data.
Raise the Technical Bar: Mentor and level up engineers across the data team through code reviews and design guidance.
Partner Across Engineering & Product: Collaborate with Software Engineering, Product, and Analytics to ensure data requirements are deeply understood at inception.
Drive adoption of data platform standards and architectural patterns across teams through clear communication, documentation, and trust-building.
Personally identify and execute fixes for deep‐seeded bottlenecks, architectural bugs, and performance issues that impact system stability.
10+ years of data engineering experience, with at least 3–5 years operating at a principal or staff IC level in a start-up environment.
~ Production-grade experience with Snowflake and dbt (required); Hands‐on AWS experience at scale, including cloud data architecture, cost governance, and infrastructure‐as‐code; Terraform experience is a nice‐to‐have.
~ DataOps experience: familiarity with CI/CD for data pipelines, automated testing frameworks, and deployment practices that treat data infrastructure like production software.
~ Significant, demonstrated AI usage in your day‐to‐day engineering work is required. it's how we expect principal‐level engineers to operate.
~ Expert‐level proficiency in Python and SQL, with strong command of distributed systems, data pipeline design, and performance optimization.
~ Strong data modeling background: dimensional modeling, schema design, data contracts, and the ability to build structures that outlast the team member

📌 Principal Data Engineer (Ciudad de México)
🏢 JobGet
📍 Ciudad de México