דרושים » דאטה » Senior Data Engineer

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 looking for a brilliant Data Engineer- an independent, logical thinker who enjoys solving complex problems and building scalable data solutions. You will own and evolve the data foundations that power Product, Finance, Operations, and Business teams. Working closely with diverse stakeholders, Analysts, and R&D teams, you will serve as a technical partner, translating complex business challenges into robust data models, pipelines, and infrastructure. You should be able to translate business and data requirements into scalable data pipelines and data models, integrating multiple internal and external data sources using the most appropriate technologies. Beyond building pipelines, you will improve data quality, modernize existing data assets, automate manual workflows, and help create a clean, reliable, and scalable data ecosystem that supports the company's growth.
Roles and Responsibilities:
Design and build end-to-end data pipelines - From defining source structures and integrating APIs to delivering clean, trusted datasets that enable analytics, reporting, and operational workflows.
Translate business needs into scalable data solutions based on business priorities and the product roadmap, understand technical requirements, and deliver purpose-built pipelines and tools.
Act as the technical partner for Product, Finance, Operations, and R&D teams, translating business needs into scalable data models, pipelines, and internal tools while ensuring reliable, trusted data across the organization.
Write high-quality, maintainable code, while following best practices and leveraging modern data tooling and CI/CD principles.
Drive data quality and reliability - Monitor, validate, troubleshoot, and continuously improve data quality and pipeline reliability across the data platform.

We are looking for a highly skilled Senior Data Platform Engineer to join our Data Platform team and play a key role in designing, building, and evolving the companys data infrastructure.

This is a hands-on engineering position focused on developing scalable data platforms, high-volume ingestion pipelines, and the foundational data systems that power analytics, product applications, AI workloads, and large-scale model execution. You will help architect our data lake, build reliable ingestion capabilities at scale, and shape the core infrastructure that supports the companys future growth.

This is an exciting opportunity to work with cutting-edge technologies and have a defining impact on the architecture and evolution of our data platform.


What Youll Do
Build complex, high-volume batch and near-real-time ingestion services while helping evolve the platforms streaming capabilities.
Design and implement data solutions for all application requirements in a distributed microservices environment
Develop and optimize large-scale data pipelines for batch and streaming use cases.
Ensure data quality, compliance, and governance.
Manage and optimize Kubernetes infrastructure, utilizing KEDA to dynamically scale resources for large-scale data processing workloads.
Own the full lifecycle of our data stack using Terraform and Kubernetes, from provisioning to application-level frameworks.
Provide technical guidance for the team, drive best practices around infrastructure, CI/CD, testing, and system design.
Deep-dive into memory management and performance tuning to ensure our large-scale distributed systems are both cost-effective and lightning-fast.

Were looking for a Senior Data Engineer in the Data Collection Ingest team to contribute to the design and coding of our data ingestion services & pipelines. This role involves working as part of a team that handles millions of requests per minute across multiple servers, and is responsible for a wide-range of data pipelines, processing billions of events each day.
Why is this role so important?
As the most trusted platform for measuring online behavior, millions of people rely on our insights daily as the ground truth for their knowledge of the digital world. Producing these insights requires large scale raw data to be ingested reliably in high scale to provide stable signals for analysis. As a Data Engineer you will have the opportunity to perform hands-on work and own our raw data ingestion pipeline end-to-end. Your work will have a direct impact on the quality and reliability of our data and the insights that our products are delivering to our customers.
So, what will you be doing all day?
Your role as Data Engineer of the Ingest Data Collection team means your daily responsibilities may include:
Design, code and manage end-to-end our data ingestion pipelines, both online and offline.
Take charge of developing & maintaining modern data infrastructure, while implementing best practices for building data pipelines.
Be responsible for high-scale ingestion services, solving challenges of availability, reliability, and scalability.
Run the production environment by monitoring availability and taking a holistic view of system health and data quality.
Own data infrastructure features from design to production using industry best practices with focus on quality and delivery.
Lead design & decision-making processes of the team.
Solve diverse complex problems of scale, performance and business logic.
Collaborate with product managers and other team leaders to plan, nurture, and implement an efficient and effective development process.
Continuously learn and evaluate new technologies in the everlasting effort to perfect our products
Perform code reviews, evaluate implementations, and provide feedback about potential improvements.
Improve your skills, learn from and mentor top-notch engineers and enrich other team members.
Have lots of fun!

We are looking for an experienced Senior Data Engineer to join our Data team. In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data/analytics platforms.
A day in the life and how youll make an impact:
Design and develop high-performance Data Pipelines and ETL processes to support diverse business needs.
Work closely with Product, Business Intelligence, Sales and other teams to integrate data solutions, ensuring seamless alignment and collaboration across functions.
Continuously improve our data analytics platforms, optimizing system performance and ensuring robust, reliable data infrastructure.
Oversee the entire data lifecycle, from infrastructure setup and data acquisition to detailed analysis and automated reporting, driving business growth through data-driven insights.
Implement robust data quality checks, monitoring mechanisms, and data governance policies to maintain data integrity and security, troubleshooting and resolving any data-related issues efficiently.

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
21679 ID
Role Description:
As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.
Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.
Key Job Responsibilities and Duties:
Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
Dealing with massive textual sources to train GenAI foundation models.
Solving issues with data and data pipelines, prioritizing based on customer impact.
End-to-end ownership of data quality in our core datasets and data pipelines.
Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.
Providing tools that improve Data Quality company-wide, specifically for ML scientists.
Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.
Acting as an intermediary for problems, with both technical and non-technical audiences.
Promote and drive impactful and innovative engineering solutions
Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

We're looking for a Data Warehouse Tech Lead to drive the technical vision and execution of our data infrastructure that powers decision-making across.
You'll lead both the technology and the business coordination for our data warehouse - architecting scalable solutions while working closely with stakeholders and data providers to ensure our platform serves the entire organization's needs. This role combines deep technical leadership with strategic business partnership as we build our next-generation data stack.
We believe three things matter for every role: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll:
Lead technical architecture - design and develop scalable data warehouse solutions that support multiple products and serve the entire organization's analytics needs
Manage the technical roadmap - set strategy and guide execution for the Data Warehouse team, ensuring our platform evolves with business requirements
Drive business process coordination - translate business needs into technical requirements while establishing clear data contracts with R&D, Analytics, and external data providers
Establish and implement best practices - set technical standards for data warehouse architecture, performance tuning, and development methodologies that guide the entire team's approach to building scalable data solutions
Create and maintain sustainable data pipelines - build resilient systems capable of handling unstructured data and managing an evolving schema registry across diverse data sources
Implement advanced data modeling - create robust data structures using methodologies like dimensional modeling, and optimize ETL/ELT processes for our semantic layer
Establish data quality standards - build processes for schema evaluation, anomaly detection, and monitoring data completeness and freshness across all sources
Lead cross-team collaboration - work directly with Data Engineers, ML Platform Engineers, Data Scientists, Analysts, and Product Managers to align technical solutions with business goals

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.

We are looking for a Data Engineer to join our team and play a key role in designing, building, and maintaining scalable, cloud-based data pipelines. You will work with AWS services , Airflow and databricks to integrate, process, and analyze large datasets, ensuring data reliability and efficiency.
Your work will directly impact business intelligence, analytics, and data-driven decision-making across the company.
What Youll Do:
ETL & Data Processing: Develop and maintain ETL processes, integrating data from various sources (APIs, databases, external platforms) using Python, SQL, and cloud technologies.
Utilize LangGraph and other frameworks to create state of the art AI agents that integrate various tools and data sources.
Implement integrations that allow agents to take automated actions on behalf of users, streamlining workflows and reducing overhead.
Data Modeling: Design and maintain logical and physical data models to support business needs.
Optimization & Scalability: Improve process efficiency and optimize runtime performance to handle large-scale data workloads.
Collaboration: Work closely with BI analysts and business stakeholders to define data requirements and functional specifications.
Monitoring & Troubleshooting: Ensure data integrity and reliability by proactively monitoring pipelines and resolving issues.