We are looking for a Backend Engineer with deep data engineering expertise to build and evolve the systems that power product, analytics, experimentation, and AI development.
You will work across production backend services and data infrastructure, taking technical ownership of the full data lifecycle - from event ingestion and real-time processing to orchestration, modeling, quality, and reliable data access. This role combines hands-on backend development with ownership of shared data platform, working closely with backend, frontend, AI, DevOps, analytics, and product teams.
What you'll do:
Build and maintain production-grade Python and Java backend services, APIs, and reactive data-processing pipelines with clear interfaces, resilient failure handling, and comprehensive observability.
Own and evolve batch and streaming data pipelines supporting product analytics, learner insights, experimentation, and AI model training.
Design and maintain trusted data models, shared metrics, and semantic-layer business logic across the analytical data platform.
Define and enforce standards for data contracts, testing, lineage, freshness, and observability.
Develop reliable approaches to schema evolution, backfills, replay, workload distribution, and failure recovery.
Collaborate with DevOps, backend, frontend, AI, analytics, and product teams to deliver reusable data capabilities and maintain clear system boundaries.

We are looking for an experienced Data Engineer to own and evolve data infrastructure and analytics platform.

You will design and build scalable data pipelines and data models, working with large and diverse datasets generated across Trigos global retail deployments. You will own the data lifecycle from ingestion and transformation through orchestration, reliability, and analytics-ready datasets.

As a key technical owner of data platform, you will also work closely with analysts, Product, R&D, Operations, and Customer Success to build trusted data products, evolve our BI semantic layer, and turn complex data into scalable solutions

A day in the life
Design, build, and maintain scalable ETL/ELT pipelines processing large and diverse datasets.
Own and evolve data architecture, from ingestion and transformation through analytics-ready data models and consumption.
Build reliable data workflows and orchestration processes, with a focus on scalability, performance, and maintainability.
Design and maintain curated data models that power analytics, operational workflows, and data-driven products.
Ensure data quality, observability, monitoring, and reliability across datasets and pipelines.
Develop and evolve the BI semantic layer, enabling trusted metrics and self-service analytics across the company.
Work closely with R&D and Product on data integrations and new data-driven capabilities, including use cases that bridge offline analytics and production systems.
Partner with analysts and business stakeholders to translate complex requirements into scalable data solutions.

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 are using technology to transform transportation around the world. From changing a single persons daily commute to reducing humanitys collective environmental footprint - weve got huge goals.
As a Senior Backend Engineer in the Routing team, you will be responsible for designing, building, and scaling the backend systems that power our companys core products. Youll work on high-impact, distributed services that manage geospatial data, large-scale databases, real-time events, and mission-critical workflows used across multiple teams and regions. Your work will directly influence system reliability, performance, and developer productivity at scale.
You will collaborate closely with Product, Infrastructure, Data, and other engineering teams to define system boundaries, evolve shared services, and ensure our backend platform can support rapid product growth. You will be part of a highly skilled and motivated engineering group responsible for building robust, scalable, and maintainable backend systems that operate in real time and at high load.
What Youll Do:
Design, build, and scale backend systems and data pipelines that power our companys GIS and mapping platform, from initial design through production deployment and long-term ownership.
Own features end-to-end with strong accountability, including design, implementation, testing, deployment, monitoring, and ongoing maintenance.
Develop and maintain Python-based data pipelines that ingest, process, and transform large-scale OpenStreetMap (OSM) and other geospatial datasets.
Model, store, and query complex geospatial data using PostgreSQL and PostGIS, including road topology, zones, polygons, points, and other business-critical spatial entities.
Optimize database schemas, spatial indexes, and queries to efficiently handle large datasets and high-throughput workloads.
Build and maintain backend services and APIs that expose geospatial and mapping data to other backend services, frontend applications, and data consumers.
Collaborate closely with other backend teams, frontend teams, data teams, and infrastructure to align on data contracts, system boundaries, and cross-team initiatives.
Improve system reliability, observability, and performance through monitoring, alerting, benchmarking, and continuous optimization.
Contribute to architectural decisions, technical standards, and best practices, while mentoring other engineers and raising the overall engineering bar.

We are seeking a Senior Backend Engineer - Data Platform to join our expanding team and play a crucial role in designing, building, and maintaining robust and scalable data pipelines and infrastructure. In this role, you will directly enable data-driven decision-making and support the development and deployment of AI/ML products that power Health.
Youll collaborate closely with engineering, product, and data science teams to ensure our data systems are high-quality, resilient, and scalable as we grow. As a Senior Backend Engineer on our Data Platform team, you will drive efforts to deliver reliable, efficient, and consistent data services across the organization. You will also help enable the rapid development and deployment of advanced features, insights, and AI-driven capabilities that improve outcomes for clinicians and clients.

We are looking for a Backend Engineer to drive the architecture and development of our AI-powered language learning platform. In this role, you will design and implement backend systems that directly impact millions of learners worldwide. You will architect scalable services that power real-time AI conversations, ensuring low-latency performance and reliability while building the foundation for our next generation of language learning features.
What Youll Do:
Design, develop, and maintain high-performance backend services and APIs (REST and gRPC) that power AI-driven conversational experiences.
Build and optimize asynchronous Python applications capable of handling real-time audio/text processing at scale.
Ensure seamless, low-latency integration between mobile and web clients and our AI backend platform.
Drive technical decisions on system architecture, focusing on low latency and fault-tolerance.
Collaborate with AI/ML, mobile, DevOps, and product teams to deliver end-to-end solutions that delight our users.
Work within CI/CD workflows to ensure smooth deployments and maintain high code quality standards.
Optimize system performance and resource utilization while maintaining reliability SLAs for our growing user base.

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.

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

Were looking for an experienced Software Engineer to join the Cato DataSec team. In this critical role, you will join a team of experienced engineers building the next generation of Data Security platform. You will design and develop large-scale backend systems that process massive amounts of security data, build data pipelines, and enable advanced analytics and AI-driven security capabilities.

Responsibilities
Design and develop scalable backend services and data-intensive applications.
Build and maintain large-scale data pipelines that process and analyze massive volumes of security data.
Own features end-to-end, from architecture and backend implementation to production deployment.
Work closely with Product Management, Research, Data Science, SOC, NOC, and Automation teams to deliver new capabilities.
Design and implement enterprise-grade platform solutions with a focus on scalability, reliability, and performance.
Research and adopt new technologies to improve data processing, analytics, and AI-driven security products.
Collaborate with multiple engineering teams across the organization.

Were looking for a Core Full-Stack Engineer who combines strong product instincts, deep engineering skills, and the ability to move fast without compromising on execution quality.
Youll be a primary technical driver, taking significant product modules from concept through design, architecture, implementation, and production. You wont just implement finished specifications, youll partner with product and design to shape the solution, make technical decisions, and own delivery across the entire stack.
Your work will turn complex, large-scale data into fast, intuitive products for Fortune 500 marketing teams. Youll build across the entire data and application stack-from data ingestion and ETL pipelines to scalable backend services, polished interfaces, and responsive visualizations. This role combines full-stack development with hands-on data engineering, owning how data is collected, transformed, stored, and served to make exploring datasets with billions of records feel effortless.
This is a hands-on role for someone who has driven meaningful product development in a fast-growing startup and is comfortable taking ownership before every requirement has been defined.
Responsibilities
Own complete product modules from concept to production.
Build and own production data pipelines.
Design scalable data models and APIs.
Build fast, polished, data-intensive interfaces.
Drive performance across the entire stack.
Ensure data quality and reliability.
Create reusable product foundations.
Own production reliability and safe delivery.
Lead through hands-on execution.