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As a Senior BI Data Engineer, youll join a company where culture isnt a slogan - its our DNA.

Youll be part of a data-driven organization where every voice matters, working at the heart of our BI team to raise the bar for analytical excellence.

In this role, youll take end-to-end ownership of complex, high-impact BI initiatives, shaping how measures success, makes decisions, and scales. Your work will directly impact over 1M users worldwide, empowering B2B sales teams to unlock new revenue opportunities and drive sustainable growth.

What Youll Actually Do:

Lead the design and evolution of BI data foundations, owning key product and GTM metric definitions and data models
Build, scale, and maintain high-quality ELT pipelines and curated datasets (raw → modeled → semantic) that power dashboards and self-serve analytics
Collaborate closely with Data Scientists to operationalize machine learning use cases, including feature pipelines and analytical datasets
Take senior ownership within the BI team by mentoring peers, reviewing critical work, and promoting best practices in analytics engineering and semantic modeling
Own the performance, cost, and reliability of the data warehouse and BI layer by optimizing models, queries, and incremental processing patterns
Translate ambiguous business questions into clear, governed metrics and scalable data models, partnering with Product, RevOps/GTM, Finance, and Analytics
Establish and maintain strong data quality, observability, and monitoring practices (tests, SLAs, anomaly detection)
Drive standardization and automation across the BI development lifecycle, including version control, CI/CD, documentation, and release processes
Stay ahead of modern BI and analytics engineering trends, including AI-assisted development, and apply them pragmatically to increase trust and speed

Your Mission As a Senior Data Engineer, your mission is to build the scalable, reliable data foundation that empowers us to make data-driven decisions. You will serve as a bridge between complex business needs and technical implementation, translating raw data into high-value assets. You will own the entire data lifecycle-from ingestion to insight-ensuring that our analytics infrastructure scales as fast as our business.

Key Responsibilities:
Strategic Data Modeling: Translate complex business requirements into efficient, scalable data models and schemas. You will design the logic that turns raw events into actionable business intelligence.
Pipeline Architecture: Design, implement, and maintain resilient data pipelines that serve multiple business domains. You will ensure data flows reliably, securely, and with low latency across our ecosystem.
End-to-End Ownership: Own the data development lifecycle completely-from architectural design and testing to deployment, maintenance, and observability.
Cross-Functional Partnership: Partner closely with Data Analysts, Data Scientists, and Software Engineers to deliver end-to-end data solutions.

Were seeking our first Data Engineer to join the Revenue Operations team. This is a high-impact role where youll build the foundations of our data infrastructure - connecting the dots between systems, designing and maintaining our data warehouse, and creating reliable pipelines that bring together all revenue-related data. Youll work directly with the Director of Revenue Operations and partner closely with Sales, Finance, and Customer Success.
This is a chance to shape the role from the ground up and create a scalable data backbone that powers smarter decisions across the company.
Role Overview:
As the Data Engineer, you will own the design, implementation, and evolution of our data infrastructure. Youll connect core business systems (CRM, finance platforms, billing systems,) into a central warehouse, ensure data quality, and make insights accessible to leadership and revenue teams. Your success will be measured by the accuracy, reliability, and usability of the data foundation you build.
Key Responsibilities:
Data Infrastructure & Warehousing:
Design, build, and maintain a scalable data warehouse for revenue-related data.
Build ETL/ELT pipelines that integrate data from HubSpot, Netsuite, billing platforms, ACP, and other business tools.
Develop a clear data schema and documentation that can scale as we grow.
Cross-Functional Collaboration:
Work closely with Sales, Finance, and Customer Success to understand their reporting and forecasting needs.
Translate business requirements into data models that support dashboards, forecasting, and customer health metrics.
Act as the go-to partner for data-related questions across revenue teams.
Scalability & Optimization:
Continuously monitor and optimize pipeline performance and warehouse scalability.
Ensure the infrastructure can handle increased data volume and complexity as the company grows.
Establish and enforce best practices for data quality, accuracy, and security.
Evaluate and implement new tools, frameworks, or architectures that improve automation, speed, and reliability.
Build reusable data models and modular pipelines to shorten development time and reduce maintenance.

Were looking for an experienced and passionate Staff Data Engineer to join our Data Platform group in TLV as a Tech Lead. As the Groups Tech Lead, youll shape and implement the technical vision and architecture while staying hands-on across three specialized teams: Data Engineering Infra, Machine Learning Platform, and Data Warehouse Engineering, forming the backbone of our companys data ecosystem.
The groups mission is to build a state-of-the-art Data Platform that drives our company toward becoming the most precise and efficient insurance company on the planet. By embracing Data Mesh principles, we create tools that empower teams to own their data while leveraging a robust, self-serve data infrastructure. This approach enables Data Scientists, Analysts, Backend Engineers, and other stakeholders to seamlessly access, analyze, and innovate with reliable, well-modeled, and queryable data, at scale.
In this role youll :
Technically lead the group by shaping the architecture, guiding design decisions, and ensuring the technical excellence of the Data Platforms three teams
Design and implement data solutions that address both applicative needs and data analysis requirements, creating scalable and efficient access to actionable insights
Drive initiatives in Data Engineering Infra, including building robust ingestion layers, managing streaming ETLs, and guaranteeing data quality, compliance, and platform performance
Develop and maintain the Data Warehouse, integrating data from various sources for optimized querying, analysis, and persistence, supporting informed decision-makingLeverage data modeling and transformations to structure, cleanse, and integrate data, enabling efficient retrieval and strategic insights
Build and enhance the Machine Learning Platform, delivering infrastructure and tools that streamline the work of Data Scientists, enabling them to focus on developing models while benefiting from automation for production deployment, maintenance, and improvements. Support cutting-edge use cases like feature stores, real-time models, point-in-time (PIT) data retrieval, and telematics-based solutions
Collaborate closely with other Staff Engineers across our company to align on cross-organizational initiatives and technical strategies
Work seamlessly with Data Engineers, Data Scientists, Analysts, Backend Engineers, and Product Managers to deliver impactful solutions
Share knowledge, mentor team members, and champion engineering standards and technical excellence across the organization.

We are looking for an experienced and visionary Data Platform Engineer to help design, build and scale our BI platform from the ground up.
In this role, you will be responsible for building the foundations of our data analytics platform - enabling scalable data pipelines and robust data modeling to support real-time and batch analytics, ML models and business insights that serve both business intelligence and product needs.
You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics.
If you enjoy architecting from scratch, turning raw data into powerful insights, and owning the full data lifecycle - this role is for you!
Responsibilities
Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability and security.
Lead the design and architecture of the data platform - from integration to transformation, modeling, storage, and access.
Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations.
Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency.
Integrate modern tools and frameworks such as Airflow, dbt, Databricks, Power BI, and streaming platforms.
Collaborate cross-functionally with product, engineering, and analytics teams to translate business needs into data infrastructure.
Promote a data-driven culture - be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from an HPE office.
Job Description:
We are looking for a highly skilled Senior Data Engineer with strong architectural expertise to design and evolve our next-generation data platform. You will define the technical vision, build scalable and reliable data systems, and guide the long-term architecture that powers analytics, operational decision-making, and data-driven products across the organization.
This role is both strategic and hands-on. You will evaluate modern data technologies, define engineering best practices, and lead the implementation of robust, high-performance data solutionsincluding the design, build, and lifecycle management of data pipelines that support batch, streaming, and near-real-time workloads.
What Youll Do
Architecture & Strategy
Own the architecture of our data platform, ensuring scalability, performance, reliability, and security.
Define standards and best practices for data modeling, transformation, orchestration, governance, and lifecycle management.
Evaluate and integrate modern data technologies and frameworks that align with our long-term platform strategy.
Collaborate with engineering and product leadership to shape the technical roadmap.
Engineering & Delivery
Design, build, and manage scalable, resilient data pipelines for batch, streaming, and event-driven workloads.
Develop clean, high-quality data models and schemas to support analytics, BI, operational systems, and ML workflows.
Implement data quality, lineage, observability, and automated testing frameworks.
Build ingestion patterns for APIs, event streams, files, and third-party data sources.
Optimize compute, storage, and transformation layers for performance and cost efficiency.
Leadership & Collaboration
Serve as a senior technical leader and mentor within the data engineering team.
Lead architecture reviews, design discussions, and cross-team engineering initiatives.
Work closely with analysts, data scientists, software engineers, and product owners to define and deliver data solutions.
Communicate architectural decisions and trade-offs to technical and non-technical stakeholders.

Required Senior Data Engineer
About us:
A pioneering health-tech startup on a mission to revolutionize weight loss and well-being. Our innovative metabolic measurement device provides users with a comprehensive understanding of their metabolism, empowering them with personalized, data-driven insights to make informed lifestyle choices.
Data is at the core of everything we do. We collect and analyze vast amounts of user data from our device and app to provide personalized recommendations, enhance our product, and drive advancements in metabolic health research. As we continue to scale, our data infrastructure is crucial to our success and our ability to empower our users.
About the Role:
As a Senior Data Engineer, youll be more than just a coder - youll be the architect of our data ecosystem. Were looking for someone who can design scalable, future-proof data pipelines and connect the dots between DevOps, backend engineers, data scientists, and analysts.
Youll lead the design, build, and optimization of our data infrastructure, from real-time ingestion to supporting machine learning operations. Every choice you make will be data-driven and cost-conscious, ensuring efficiency and impact across the company.
Beyond engineering, youll be a strategic partner and problem-solver, sometimes diving into advanced analysis or data science tasks. Your work will directly shape how we deliver innovative solutions and support our growth at scale.
Responsibilities:
Design and Build Data Pipelines: Architect, build, and maintain our end-to-end data pipeline infrastructure to ensure it is scalable, reliable, and efficient.
Optimize Data Infrastructure: Manage and improve the performance and cost-effectiveness of our data systems, with a specific focus on optimizing pipelines and usage within our Snowflake data warehouse. This includes implementing FinOps best practices to monitor, analyze, and control our data-related cloud costs.
Enable Machine Learning Operations (MLOps): Develop the foundational infrastructure to streamline the deployment, management, and monitoring of our machine learning models.
Support Data Quality: Optimize ETL processes to handle large volumes of data while ensuring data quality and integrity across all our data sources.
Collaborate and Support: Work closely with data analysts and data scientists to support complex analysis, build robust data models, and contribute to the development of data governance policies.

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 Data Engineer to join our DataWarehouse team in TLV.
In this role, you will play a pivotal role in the Data Platform organization, leading the design, development, and maintenance of our data warehouse. In your day-to-day, youll work on data models and Backend BI solutions that empower stakeholders across the company and contribute to informed decision-making processes all while leveraging your extensive experience in business intelligence.
This is an excellent opportunity to be part of establishing our companys state-of-the-art data stack, implementing cutting-edge technologies in a cloud environment.
In this role youll
Lead the design and development of scalable and efficient data warehouse and BI solutions that align with organizational goals and requirements
Utilize advanced data modeling techniques to create robust data structures supporting reporting and analytics needs
Implement ETL/ELT processes to assist in the extraction, transformation, and loading of data from various sources into the semantic layer
Develop processes to enforce schema evaluation, cover anomaly detection, and monitor data completeness and freshness
Identify and address performance bottlenecks within our data warehouse, optimize queries and processes, and enhance data retrieval efficiency
Implement best practices for data warehouse and database performance tuning
Conduct thorough testing of data applications and implement robust validation processes
Collaborate with Data Infra Engineers, Developers, ML Platform Engineers, Data Scientists, Analysts, and Product Managers.