Required Senior Data Engineer
Description
We are making the future of Mobility come to life starting today.
We support the worlds largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization.
At the heart of our platform lies the data infrastructure, driving advanced machine learning models and optimization algorithms. As the owner of data pipelines, you'll tackle diverse challenges spanning optimization, prediction, modeling, inference, transportation, and mapping.
As a Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform-supporting real-time optimization, advanced analytics, and machine learning applications.
What You'll Do:
Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools.
Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets.
Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle.
Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows.
Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability.
Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions.
Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability.

We're looking for an experienced Software Engineer to join our Data Platform Group. In this key role, you will build the company data platform: cloud-based microservices and data pipelines that process on the order of 1M records/sec at low latency. Because the platform is the foundation other groups build on, your work has a direct impact on our customers and enables engineering, product, and research teams across the organization.
The Lakehouse team owns the data itself - how it is stored, organized, retained, and served. We own our analytical storage layer, the batch processing built on top of it, and the APIs through which customers and the rest of the company consume data. If you enjoy the problems that only appear at petabyte scale - physical data layout, query performance, storage cost, and retention - this is the role.

Responsibilities:
End-to-end ownership of our large-scale analytical storage layer: data modeling, schema and table design, partitioning, retention, and query performance.
Design and develop the batch processing layer over our data lake using Spark on EMR (Java and PySpark).
Build and evolve Java/Spring Boot services that expose our data through well-defined APIs to customers and to consumers across the company.
Own performance and cost: query optimization, file layout and compaction, cluster sizing, and storage efficiency at scale.
Research new technologies in the lakehouse and analytical-storage space and adapt them for use in our product.
Work closely with product, DevOps, and security teams.