Required Data Engineering Lead
Why this role matters:
Data isn't just for dashboards - it's the nervous system of our future products. As we expand into smart lighting (Oasis) and living tech (Easyplant), we need to process massive streams of IoT signals and power real-time AI. You won't just maintain a warehouse; you will architect the platform that connects our physical products to the digital world.
What youll do:
Build the "Brain": Architect the next-gen data platform capable of handling high-volume IoT streams and integrating AI capabilities into our daily operations.
Code the Core: This is a hands-on role. You wont just manage; youll be deep in Python, DBT, and Airflow, building and optimizing the pipelines yourself.
Scale for Complexity: We are growing from one product to a multi-brand ecosystem. You will ensure our data infrastructure evolves to support diverse business lines without becoming a bottleneck.
Democratize Insights: Build a system so robust and intuitive that every engineer, product manager, and marketer can self-serve the data they need to build better products.

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

At our company, business users don't file ticket requests for dashboards. They ask questions in natural language and get answers directly - through Claude, connected to Looker and our other data tools over MCP. Claude is also our primary tool for writing code here.

That only works if what sits underneath is right. This role owns that foundation: the event instrumentation in our products, the pipelines feeding the warehouse, the models that give the data meaning, and the tests that prove it's correct. You're not producing charts - you're making hundreds of self-served answers trustworthy.

It's an engineering role, but not one you can do from behind the data. You'll need to understand the business well enough to know what a metric should mean before you model it.

Responsibilities
Instrumentation - Own product event tracking end-to-end: define the tracking plan with Product, drive implementation with our frontend and backend teams, audit what's actually firing, and build validation that catches regressions at deploy time rather than three months later.
Pipelines and warehouse - Build and operate batch and streaming pipelines into the warehouse; own its transformation layer, performance, data quality monitoring, and alerting.
Semantic layer - Own metric definitions and the modeled layer our AI and BI tooling query against, so core business terms resolve to one thing company-wide. Design models that hold up under open-ended questions, not just the ones you anticipated. Own lineage, documentation, and governance.
Business partnership - Work directly with Product, Sales, Customer Success, and Finance to understand what they're trying to measure, and push back when a proposed metric won't survive contact with reality.
Our stack
Redshift, Looker, Airflow, Kafka, AWS, Kubernetes, Python, SQL, TypeScript - with Claude and MCP as the layer connecting people to all of it.

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.

We're hiring a Data Engineer to architect and own the systems that turn raw signal from merchants, customers, and experiments into something the whole company can build on.
Data is at the heart of everything we do - it powers our AI models, drives merchant-facing analytics, and informs how we make product decisions every day.
This is a high-ownership role for someone who wants to shape data architecture from the ground up- not inherit someone else's roadmap. You'll be the person who makes sure our data is fast, trustworthy, and easy for every team to use.
What You'll Do:
Architect and build scalable data pipelines processing millions of events daily from merchant stores, product usage, experiments, and third-party integrations
Own our data warehouse strategy end-to-end- reliability, performance, and scalability as we grow
Define our event architecture and set the standards for how the company captures and models product and business data
Build data quality practices- monitoring, validation, testing, and alerting- that the rest of the team can trust
Partner directly with Product and Engineering to unlock the data behind our AI features, merchant analytics, and experimentation
Build self-service tooling so other teams can query and analyze data without depending on you as a bottleneck
Set the bar for data modeling, orchestration, and documentation across the org

As our Founding Data Engineer, you will be the first dedicated Data Engineer at the company, with the opportunity to build our data engineering function from the ground up. You'll own the architecture and foundations of our internal data platform - the central warehouse, pipelines, and models every team relies on to understand how our product is performing, and provide tools for day to day operations. You'll define how data from across the company is collected, modeled, transformed, and governed, and establish the standards and best practices that will scale with us as we grow. Working closely with Engineering, Data Science, Delivery, Product, and Analytics teams, you'll turn data into reliable, structured assets that power product and business decisions.

What Youll Do
Build our Data Engineering Function: Be the first dedicated Data Engineer and will be required to be opinionated about the warehouse architecture, tooling, standards, and best practices for how we work with data across the company.
Develop Data Pipelines: Design, build, and maintain data pipelines that transform company data into structured, queryable assets.
Enable Data-Driven Decisions: Partner with Engineering, Data Science, Product and Analytics teams to deliver trusted data foundations and actionable insights.
Create BI Dashboards: Build and maintain dashboards and reporting layers that support product, and operational decision-making.
Ensure Data Quality and Accessibility: Implement best practices for data modeling, governance, monitoring, and reliability.

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.

we are on a mission to create public transportation systems that provide far greater access to jobs, healthcare, and education. Our platform serves as the technology backbone for modern transit networks, transforming antiquated and siloed public transportation systems into smart, data-driven, and efficient digital networks. With hundreds of agency partners around the world, we are recognized as the leading transportation technology and service provider globally.
As a Software & Data Engineer at our company, you will play a central role in building and evolving the data infrastructure that powers one of the world's leading transportation platforms. You'll work with rich, complex transportation data - from ride bookings and payments to real-time operational signals - designing the pipelines and systems that turn raw data into clean, reliable, and actionable insight across the business. This is a high-ownership role on a talented and deeply motivated engineering team in Tel Aviv, where your work will directly shape how our company understands and improves its service for cities and riders around the world.
About the Role:
Design and build highly scalable, reliable data pipelines that serve clean, structured data across our company's engineering, product, and business teams - ensuring the entire organisation can trust the data it works with
Own the architecture of complex data models that translate messy, real-world transportation data - bookings, payments, driver activity, and more - into systems that are fast, efficient, and built to scale
Lead end-to-end development across the full data lifecycle: from architecture and design through to deployment, monitoring, and continuous improvement
Proactively monitor data quality and reliability, identifying and resolving discrepancies before they reach stakeholders - building the kind of data infrastructure people can depend on
Collaborate with a broad forum of engineers, analysts, and business stakeholders to identify data needs, design POCs, and ship scalable solutions that make a real difference to how our company operates
Contribute to the adoption and evolution of our modern data stack, including DBT, Airflow, Iceberg storage, and AWS big data tooling such as Glue, EMR, and Athena.

Were looking for an Infrastructure Data Engineer to join our Data Labs (DL) department, which specializes in professional services for our super-premium customers. This role will report to our DL (Datalabs) Infrastructure Team Manager in the R&D.
Why is this role so important?
A data-focused company, and our unique AI and machine learning capabilities are the center of our business.
As a Data Infrastructure Engineer in the Datalabs group, you will be the technical backbone supporting our data production, analyst, and data science teams. You will own and maintain our AWS cloud accounts and Databricks workspaces, optimize pipeline executions, and proactively modernize our tech stack to reduce tech debt - ensuring our platform remains scalable, reliable, fast, and cost-effective.
Given the business-oriented nature of Datalabs, you will work closely with cross-functional stakeholders and internal teams who interface directly with strategic customers. By unblocking operational bottlenecks and building robust tooling, you will empower our analysts and data scientists to deliver custom, high-value data products to our customers seamlessly.
So, what will you be doing all day?
Environment & Workspace Management: Administering and optimizing our groups AWS cloud accounts, IAM policies, and Databricks workspaces to ensure peak performance, rock-solid security, and cost efficiency.
Execution & Pipeline Infrastructure: Owning the health, reliability, and scaling of our core data execution environments (Airflow, Docker, EKS/ECS, distributed queues, and database engines).
Tech Debt Modernization: Proactively researching, benchmarking, and replacing legacy debt with modern technologies to keep our platform fast and cutting-edge.
Developer & Production Support: Serving as the technical focal point for internal analysts and data science teams-providing the tooling, standards, and infra support they need to build custom client reports smoothly.
Cross-Group & Client Alignment: Partnering with external engineering teams and internal stakeholders to support custom data delivery pipelines and uphold high-priority customer SLAs.

Were looking for a talented Data Engineer to join our AI team and build the data foundations that power our AI systems at scale. As a key member of the AI team, youll be responsible for designing, building, and operating the data layer that enables our AI models and agents to perform in production. This includes data ingestion, storage, transformation, monitoring, and defining the data strategy that ensures our AI applications receive high-quality, reliable, and timely data.

Were seeking an ambitious Data Engineer who is passionate about building robust data infrastructure, working with large-scale and complex datasets, and partnering closely with AI engineers and researchers to translate model needs into production-grade data systems.


WHAT YOU WILL DO
Design, build, and operate scalable data pipelines (batch + streaming) that power our AI systems in production.
Own the end-to-end data lifecycle: ingestion, storage, transformation, serving, and continuous improvement.
Build and maintain the data layer from raw data to semantically accessible data that enables AI models and agents to perform reliably at scale.
Ensure high data quality and high-standard operation through monitoring, alerting, and validation checks.
Work with modern storage systems (data lakes, warehouses, relational, graph, and vector databases) to support diverse AI workloads.
Partner closely with AI engineers and researchers to translate model requirements into production-grade data infrastructure.
Define and evolve the data strategy to for AI applications.