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We built a platform that builds businesses. From launching and managing to growing a business, we are the all-in-one solution that empowers any business owner. Our platform services over 40 million small businesses. Our "Guidance Engine" assesses each new business introduced to our platform and devises a tailored plan that allows you to manage all your business needs from a single dashboard. Through our business-building platform, were turning the process of starting, managing, and growing a business into a better experience; were simplifying the business journey. At Tailor Brands, we believe in more than just handing you another tool; we are dedicated to teaching you the art of building a business.
About the Role:
As a data Engineer, you'll design and maintain the data pipelines that power decision-making across product, marketing, and finance. Working with Apache Airflow, Python, and SQL, you'll build trusted, well-documented datasets that enable self-service analytics for the entire organization. You'll champion data quality through validation and monitoring, optimize infrastructure for scale, and be part of a forward-thinking team that actively leverages AI tools like Claude to push the boundaries of modern data engineering. If you thrive on turning messy data into reliable, actionable foundations - and want to do it for a platform serving over 40 million small businesses - this role is for you. What were looking for:
* Design, implement and maintain pipelines that deliver data with measurable quality and SLAs for our product, marketing, finance, and other departments
* Partner with business domain experts, data analysts, and engineering teams to build foundational datasets that are trusted, well-understood, aligned with business strategy and enable a self-service analysis
* Focus on data quality, detect data /analytics quality issues, implement bug fixes and data validations for prevention
* Identify, design, and implement internal process improvements such as automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc
* Be part of a team that actively integrates AI tools like Claude into day-to-day workflows, pushing the boundaries of how data engineering gets done

a Senior Data Engineer should have strong communication and collaboration abilities, as they will be working closely with other members of the data and analytics team, as well as other stakeholders, to identify and prioritize data engineering projects and to ensure that the data infrastructure is aligned with the overall business goals and objectives.

What You'll Do
Work closely with data scientists/analytics and other stakeholders to identify and prioritize data engineering projects and to ensure that the data infrastructure is aligned with business goals and objectives
Design, build and maintain optimal data pipeline architecture for extraction, transformation, and loading of data from a wide variety of data sources, including external APIs, data streams, and data stores.
Continuously monitor and optimize the performance and reliability of the data infrastructure, and identify and implement solutions to improve scalability, efficiency, and security
Stay up-to-date with the latest trends and developments in the field of data engineering, and leverage this knowledge to identify opportunities for improvement and innovation within the organization
Solve challenging problems in a fast-paced and evolving environment while maintaining uncompromising quality.
Implement data privacy and security requirements to ensure solutions comply with security standards and frameworks.
Enhance the team's dev-ops capabilities.

Experienced Data Engineer? Excellent team player?
Come join a great team to overcome meaningful cyber challenges!
Our Data Solutions Group is looking for an exceptional Python developer with Data Engineering experience.

We're looking for a hands-on Data & AI Product Engineer to join the Product team and help shape our next generation of AI products, AI infrastructure, and cyber data insights.

You'll be our first dedicated data and AI expert inside Product, working closely with R&D, GTM, and leadership. The role sits where cybersecurity, strong coding, data engineering, AI model development, and product thinking meet. You'll identify where AI can create real customer value, prototype AI-native capabilities, train and evaluate models, and push R&D with practical ideas grounded in product data, customer behavior, and cyber insight. In parallel, you'll build the foundation for product usage monitoring, KPI infrastructure, and ongoing alerts, giving Product, R&D, Sales, Customer Success, and leadership a trusted basis for decisions. This is a build-first role for someone who wants to code, explore, analyze, and shape the future of AI in cybersecurity.

Responsibilities:
Prototype AI-driven product concepts and turn ideas into concrete experiments, working with LLMs, agents, and ML models to support future product capabilities.
Build reusable AI and data infrastructure that future product development can stand on.
Analyze product, usage, customer, and cybersecurity data to surface patterns that improve security outcomes, adoption, and product effectiveness.
Translate complex data and cyber patterns into clear recommendations for Product, R&D, GTM, and leadership.
Own the product data foundation end to end: usage, adoption, customer behavior, KPIs, and operational metrics, and build the alerts that flag meaningful changes.
Create a trusted source of truth for product usage, adoption, engagement, and product value metrics.
Support GTM teams with usage insights, adoption analysis, and data-backed narratives, and contribute to the team's product and AI strategy.

We are looking for a Data Engineer to join our team and help shape Trigos data platform.

You will own the data layer, evolve our BI semantic layer on a modern BI platform, and enable teams across the company to access and trust their data. Working closely with analysts and stakeholders in Operations, Product, R&D, and Customer Success, youll turn complex data into insights that drive performance and customer value.

A day in the life
Build and expand analytics data stack.
Design and maintain curated data models that power self-service analytics and dashboards.
Develop and own the BI semantic layer on a modern BI platform.
Collaborate with analysts to define core metrics, KPIs, and shared business logic.
Partner with Operations, R&D, Product, and Customer Success teams to translate business questions into scalable data solutions.
Ensure data quality, observability, and documentation across datasets and pipelines.
Support complex investigations and ad-hoc analyses that drive customer and operational excellence.

We are looking for a Senior Data Platform Engineer to join our growing Platform team and help build the foundation that powers our products, analytics, and data-driven decision making.

In this role, you will design, build, and operate the core data infrastructure that enables engineering teams to move fast while maintaining reliability, scalability, and operational excellence. You will work across databases, streaming systems, data lakes, cloud infrastructure, and platform services, solving complex challenges around scale, performance, and resilience.

As a senior engineer, you will play a key role in shaping our data architecture, driving technical decisions, and mentoring other engineers.

Responsibilities
Design, build, and operate scalable data platform services and infrastructure.
Develop high-quality software for distributed systems and data-intensive applications.
Design and optimize data storage solutions across operational databases, data lakes, and analytical systems.
Drive database performance improvements through query optimization, indexing strategies, schema design, and capacity planning.
Build and maintain event-driven architectures and streaming data pipelines.
Develop and improve data ingestion, transformation, orchestration, and processing frameworks.
Partner with product, engineering, analytics, and AI teams to deliver scalable and reliable data solutions.
Improve platform reliability, observability, and operational excellence through automation and engineering best practices.
Build and enhance CI/CD pipelines, deployment processes, and developer tooling.
Lead architecture discussions and contribute to long-term platform strategy.
Troubleshoot complex production issues and drive root-cause analysis and preventative improvements.
Mentor engineers through code reviews, technical guidance, and knowledge sharing.

Were looking for a Data & Analytics Team Lead to manage and grow our data team and own the data layer behind our AI. Youll lead a team of data analysts responsible for neural-network model analysis, model performance tracking, customer-facing and internal BI. Youll act as both the people manager and the technical lead for the team, setting technical direction while staying hands-on.

Youll thrive in this role if youre passionate about turning data into better AI products, enjoy solving complex analytical problems, and like working at the intersection of Data, Analytics, and Artificial Intelligence.

Responsibilities:

Lead the data strategy supporting AI model development, evaluation, and continuous improvement
Define KPIs and monitoring frameworks for AI model performance.
Analyze NN model outputs, build and maintain model-tracking and performance-evaluation workflows
Drive close calibration with the Algo, Product, and Data Engineering teams to align data definitions, pipelines, and metrics
Stay hands-on with SQL, Python, BI tooling, and the modern data stack
Build and own agentic data workflows - designing the context, tooling, and data access that let AI agents reliably query, analyze, and act on company data

We are looking for a motivated and business-oriented Data Engineer to join our global Data & BI team.
In this role, you will design, develop, and maintain modern data pipelines and data solutions that support critical business processes across the organization.
You will work closely with global stakeholders, integrate data from multiple sources, ensure data quality and reliability, and contribute to modern data and AI initiatives.
(*Please note, the internal title for this role is Senior Data & BI Specialist, Business Technology.)
Responsibilities
Design, build, and maintain scalable ETL/ELT pipelines
Develop and optimize complex SQL queries and data transformation processes
Integrate data from internal and external systems, including APIs, databases, and third-party platforms
Own data quality, monitoring, reliability, and governance processes
Investigate and resolve data-related issues while supporting business users
Take ownership of data workflows from design and development through production support
Collaborate with global stakeholders to understand business requirements, analyze data discrepancies, and deliver reliable data solutions
Design, develop, and maintain AI-powered data solutions that support business decision-making and operational excellence
Build and implement monitoring capabilities (e.g. anomaly detection, forecasting, etc.) using modern AI capabilities
Partner with business teams to identify opportunities for applying AI and LLM-based solutions to real-world business challenges
Develop and operationalize AI workflows and data products from proof-of-concept through production deployment.

The Data-Nexus team, a central part of the R&D department , is seeking a versatile Senior Data Engineer to take part in leading data vision using a lake-house architecture, and building high-scale solutions for handling and consuming the different data assets of the company.
In this role youll go beyond traditional boundaries, taking on full product responsibilities - from conceptualization and architecture to design, maintenance, and continuous development. You will collaborate closely with stakeholders across the company to ensure our solutions effectively meet their needs.
The ideal candidate will have strong data engineering capabilities, along with software development skills including understanding of designing and managing AWS cloud infrastructure and strong knowledge of different data architectures, methods and tools.
Responsibilities:
Design and development of scalable, reliable, and secure data infrastructure and build ETL pipelines that handle diverse clinical data for research. Write production SQL, Spark jobs and craft schemas that evolve gracefully as research and production questions change.
Automate releases with CI/CD and Infrastructure as Code.
Optimise throughput, latency and cloud cost to meet research timelines at large scale.
Develop and maintain high-quality, scalable, and efficient code while fostering a culture of continuous improvement through regular retrospectives and knowledge sharing.
Take ownership of the full development lifecycle, including requirement gathering, design, implementation, testing, deployment, and ongoing maintenance.
Collaborate with cross-functional teams, including data scientists, data analysts, product managers, regulatory teams, and other developers, to drive the development of new tools and features that support mission.
Explore and adopt new technologies and frameworks that can enhance the capabilities of the Data-Nexus team and the overall data projects.

we are looking for a AI Solution Engineer.
The AI Solutions Engineer plays a critical, high-impact role in scaling our clinical AI solutions and ensuring the ability to deploy high quality products, monitor and support them in production.
This position requires strong software and data engineering skills to elevate our operational systems.
You will be responsible for independently driving complex E2E projects, demonstrating flexible thinking and making cost-effective decisions while balancing quality versus speed.
You will serve as a vital technical bridge, forming strong collaborations with both the R&D and Product organizations. Success in this dynamic and fast-driven environment demands a focused "getting things done" and "can-do" attitude to deliver value with speed and quality.
Responsibilities:
Automate and streamline AI operations - develop tools to automate AI deployment, data validation, and reporting for smooth production integration of AI solutions built by our research teams.
Act as the production technical expert - collaborate with Product and R&D teams during feature design to ensure production feasibility, scalability, and operational excellence.
Lead end-to-end tool development - gather requirements, design, implement, and deploy operational tools and pipelines in collaboration with cross-functional teams.
Drive engineering quality and play a key role in team member mentoring - uphold and develop engineering standards while fostering innovation.
Investigate and resolve complex data challenges - analyze and address production data issues, including data quality, availability, performance anomalies, and integration challenges.
Collaborate with customer-facing teams - understand real-world operational challenges, develop scalable solutions, and bridge the gap between customer needs and AI system behavior.
Ensure ongoing AI model reliability - design and implement mechanisms for monitoring data and performance degradation, and maintaining long-term model effectiveness.
Develop monitoring and analytics frameworks - create metrics and dashboards to track production health, data integrity, and AI performance across the deployment lifecycle.

We're looking for a Data Scientist to turn our network data into a competitive advantage. You'll work directly with terabytes of telecom signaling, session, and usage data to build models that improve network selection, detect anomalies, optimize cost-per-gigabyte, and surface insights that shape product and commercial strategy. This is a high-impact role sitting at the intersection of ML, telecom engineering, and business decision-making.

What You'll Do

Design and deploy machine learning models on network data - including network selection optimization, churn and usage prediction, fraud and anomaly detection, and QoS/QoE forecasting.

Analyze CDRs, signaling events (Diameter, GTP, SIP), session logs, and radio-level metrics to identify performance issues, cost drivers, and growth opportunities.

Partner with the Network Operations and Product teams to translate raw telemetry into actionable signals - e.g., which carrier to steer traffic to in a given country at a given hour.

Build forecasting and segmentation models that inform pricing, capacity planning, and customer lifecycle strategy.

Own analyses end-to-end: framing the business question, exploring the data, building the model, validating it, and communicating findings to both technical and executive audiences.

Define and track KPIs for network quality, customer experience, and commercial performance, and build dashboards that make those metrics visible across the company.

Mentor junior data scientists and analysts, and help raise the bar on data science practices, code quality, and experimentation rigor.

Were looking for a Senior Data Science who is excited about designing and building production-grade AI systems powered by modern LLMs and machine learning.

This role is ideal for someone who enjoys working at the intersection of AI, engineering, and product, and who is passionate about turning cutting-edge AI capabilities into reliable, scalable systems that solve real customer problems.

Youll work closely with product managers, data scientists, and engineers to design, build, and deploy AI-powered solutions - including LLM pipelines, agents, and intelligent automation systems that power our core products.

This is a hands-on role where youll take ownership of the full lifecycle of AI features - from problem framing and architecture design to deployment, evaluation, and iteration in production.

Responsibilities
Design and build AI-powered systems that leverage LLMs, embeddings, and modern NLP techniques to transform raw product data into structured, actionable insights.
Develop and maintain production-grade AI pipelines including prompt workflows, agents, retrieval systems (RAG), and automated decision processes.
Work closely with product and engineering teams to translate business needs into scalable AI solutions.
Architect systems that combine LLMs, data pipelines, and traditional ML into robust end-to-end products.
Experiment with and integrate new AI tools, models, and frameworks to continuously improve system capabilities and performance.
Own the full lifecycle of AI features - from design and prototyping to deployment, monitoring, and iteration.
Ensure reliability and performance of AI systems in production, including evaluation frameworks, guardrails, and monitoring.
Collaborate across teams to define best practices for AI system design, prompt engineering, and agent orchestration.
Collaborate closely with cross-functional team members, effectively communicate complex ideas, share knowledge, and mentor engineers and data scientists to elevate team standards and impact.

Were looking for a Principal Backend & Data Engineer to own and evolve the core data-intensive backend architecture of our AI platform. This is a senior individual contributor role with broad architectural responsibility and deep hands-on impact.

You will be a technical authority in building scalable, high-performance backend and data systems that power our AI research engine - working closely with AI scientists, platform engineers, and product leaders. Beyond implementation, you will define technical direction, set engineering standards, and influence how we build and scale data-driven AI systems across the organization.

What Youll Do
Architecture & Technical Ownership

Own the architecture of core backend and data systems that support large-scale, AI-driven computation and inference.
Design and evolve high-performance analytical processing layers handling complex data workloads at scale.
Make and drive long-term architectural decisions across backend, data, and AI integration domains.
Define standards and best practices for performance, scalability, reliability, and data correctness.
Backend & Data Engineering

Design, implement, and optimize data-intensive backend services using Python.
Build and evolve analytical compute pipelines using technologies such as Polars, DuckDB, AirFlow, Iceberg, Spark and other ingestion and processing frameworks.
Collaborate closely with AI scientists to productionize algorithms and embed advanced intelligence into the platform.
Platform & Infrastructure

Architect and maintain containerized systems using Docker and Kubernetes.
Ensure systems are observable, debuggable, and operable in production environments.
Drive performance tuning and cost-efficiency across compute-heavy workloads.
Technical Leadership

Act as a technical authority and thought leader within the engineering organization.
Mentor senior engineers through design reviews, architectural guidance, and hands-on collaboration.
Serve as a force multiplier by unblocking complex technical challenges and raising the overall engineering bar.

abra professional services is seeking a Data Analytics Architect / Principal Data Engineer We are looking for a skilled Data Analytics Architect / Principal Data Engineer to lead the organization’s analytics data domain, design large-scale data architecture, build advanced analytical models, define organizational data modeling standards, and implement innovative AI and LLM-based technologies. This role requires deep expertise in data engineering, analytics, BI, SQL, PostgreSQL, data modeling, performance optimization, data governance, and the integration of AI tools into data engineering processes. A full-time hybrid position based in Central Israel. The first 4–6 months will be based in Tel Aviv, followed by a transition to the Lod area. The position includes 1 day a week remote. Key Responsibilities:
* Design and develop data architecture that supports large-scale analytics and business intelligence.
* Lead the design and implementation of complex data models and enterprise data modeling solutions.
* Define organizational standards, methodologies, and best practices in the data domain.
* Optimize database performance, analytical queries, and reporting processes.
* Lead LLM-based development and implement AI tools within data engineering processes.
* Mentor data engineers, conduct code reviews and architecture reviews.
* Collaborate with management and business stakeholders to build a technological roadmap.
* Establish frameworks for data quality and data governance.
* Lead initiatives to improve the reliability and stability of BI and analytics systems.
* Evaluate new technologies and lead the adoption of innovative solutions across the organization.
* Lead the design and implementation of end-to-end analytics platforms.