דרושים » דאטה » דרוש /ה מפתח /ת תוכנה - TABLEAU ( BI )

In this role, youll own the engineering backbone that enables our data engineers & data scientists to experiment, innovate, and deploy production-ready models, ensuring our systems are robust, scalable, and future-proof.

What You'll Actually Do:

Lead and mentor a team of Data Engineers, fostering a culture of technical excellence, accountability, and collaboration.
Define and drive the teams technical roadmap, standards, and best practices for data modeling, performance, and reliability.
Design, build, and evolve scalable embedding pipelines and recommendation infrastructure, from algorithm tuning and training workflows to production deployment.
Manage and optimize vector db, ensuring efficient indexing strategies, search performance, and low-latency retrieval.
Lead the development of retrieval services leveraging Approximate Nearest Neighbor (ANN/KNN) techniques such as HNSW, IVF, and Product Quantization (PQ).
Ensure reliability, observability, and scalability across all real-time data systems powering our recommendation engine.
Collaborate with Data Science, Product, and Analytics to operationalize models, validate KPIs, and translate technical insights into actionable product improvements.

Design, implement, and maintain robust data pipelines and ETL/ELT processes on GCP (BigQuery, Dataflow, Pub/Sub, etc.).
Build, orchestrate, and monitor workflows using Apache Airflow / Cloud Composer.
Develop scalable data models to support analytics, reporting, and operational workloads.
Apply software engineering best practices to data engineering: modular design, code reuse, testing, and version control.
Manage GCP resources (BigQuery reservations, Cloud Composer/Airflow DAGs, Cloud Storage, Dataplex, IAM).
Optimize data storage, query performance, and cost through partitioning, clustering, caching, and monitoring.
Collaborate with DevOps/DataOps to ensure data infrastructure is secure, reliable, and compliant.
Partner with analysts and data scientists to understand requirements and translate them into efficient data solutions.
Mentor junior engineers, provide code reviews, and promote engineering best practices.
Act as a subject matter expert for GCP data engineering tools and services.
Define and enforce standards for metadata, cataloging, and data documentation.
Implement monitoring and alerting for pipeline health, data freshness, and data quality.

we are looking for a Product Owner -Software Engineer.
As a Product Owner / Developer, you will play a dual role - defining business and product requirements while contributing directly to the technical delivery. Youll partner with collaborators across engineering, data, and business teams to translate goals into actionable roadmaps and measurable results.
Key Responsibilities:
Gather and define requirements aligned with strategic business goals and customer value.
Help the team leaders maintain and prioritize the product backlog, ensuring clarity, transparency, and focus on impact.
Collaborate with engineering teams to design and build scalable data and automation solutions (50%+ of the time)
Contribute to development, testing, and deployment of modern data pipelines using Apache Airflow, Apache Spark, and AWS cloud services (ECS, Lambda, Glue, S3).
Balance short-term delivery objectives with long-term technical vision.
Track and communicate key performance metrics that demonstrate business outcomes.

It starts with you - an engineer driven to build modern, real-time data platforms that help teams move faster with trust. You care about great service, performance, and cost. Youll architect and ship a top-of-the-line open streaming data lake/lakehouse and data stack, turning massive threat signals into intuitive, self-serve data and fast retrieval for humans and AI agents - powering a unified foundation for AI-driven mission-critical workflows across cloud and on-prem.
If you want to make a meaningful impact, join mission and build best-in-class data systems that move the world forward - this role is for you.
:Responsibilities
Build self-serve platform surfaces (APIs, specs, CLI/UI) for streaming and batch pipelines with correctness, safe replay/backfills, and CDC.
Run the open data lake/lakehouse across cloud and on-prem; enable schema evolution and time travel; tune partitioning and compaction to balance latency, freshness, and cost.
Provide serving and storage across real-time OLAP, OLTP, document engines, and vector databases.
Own the data layer for AI - trusted datasets for training and inference, feature and embedding storage, RAG-ready collections, and foundational building blocks that accelerate AI development across the organization.
Enable AI-native capabilities - support agentic pipelines, self-tuning processes, and secure sandboxing for model experimentation and deployment.
Make catalog, lineage, observability, and governance first-class - with clear ownership, freshness SLAs, and access controls.
Improve performance and cost by tuning runtimes and I/O, profiling bottlenecks, planning capacity, and keeping spend predictable.
Ship paved-road tooling - shared libraries, templates, CI/CD, IaC, and runbooks - while collaborating across AI, ML, Data Science, Engineering, Product, and DevOps. Own architecture, documentation, and operations end-to-end.