לוח משרות דרושים מהנדס/ת דאטה/DATA ENGINEER משרה מלאה, לחיפוש זה נמצאו 131 משרות

We are looking for a Data Science Engineer to design and implement the decision-making logic of our Price Optimizer. You will focus on translating complex business rules into mathematical models and production-ready Python code.

Your primary mission is to build the "brain" of our system. You will work at the intersection of Data Science and Product, ensuring our simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics. While you will write production code, you will rely on our Data Engineers for ETL pipelines orchestration and distributed compute scaling.

Responsibilities:

Implement Business Logic: Translate intricate pricing rules and commercial strategies into robust Python code and mathematical constraints.
Refine Optimization Models: Develop and tune the simulation and revenue management algorithms that drive our pricing recommendations.
Write Clean Code: Contribute high-quality, tested, and maintainable code to the core logic repositories.
Analyze & Improve: Use data to validate model behavior and identify edge cases where business rules clash with algorithmic outputs.
Collaborate: Partner with Solution Architects to define logic requirements and with Data Engineers to integrate your models into the Dagster pipelines.

We are looking for a Data Science Engineer to design and implement the decision-making logic of our Price Optimizer. You will focus on translating complex business rules into mathematical models and production-ready Python code.

Your primary mission is to build the "brain" of our system. You will work at the intersection of Data Science and Product, ensuring our simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics. While you will write production code, you will rely on our Data Engineers for ETL pipelines orchestration and distributed compute scaling.

Responsibilities:

Implement Business Logic: Translate intricate pricing rules and commercial strategies into robust Python code and mathematical constraints.
Refine Optimization Models: Develop and tune the simulation and revenue management algorithms that drive our pricing recommendations.
Write Clean Code: Contribute high-quality, tested, and maintainable code to the core logic repositories.
Analyze & Improve: Use data to validate model behavior and identify edge cases where business rules clash with algorithmic outputs.
Collaborate: Partner with Solution Architects to define logic requirements and with Data Engineers to integrate your models into the Dagster pipelines.

We're not looking for someone who fits neatly into a box. We're looking for someone who finds neat boxes a little underwhelming.
You're a builder, not just an advisor - analysis without artifacts isn't enough.
You close loops. You ship things - You leave systems better than you found them.
You're customer-obsessed - you understand that what happens in enterprise POVs isn't a distraction from the real work. It is the real work. You run toward hard customer problems rather than away from them.
You're opinionated and direct - you push back when something is wrong, change your mind when shown better evidence, and write and speak clearly without filler.
You move without waiting to be told - when something is broken or missing, you identify it, own it, and fix it. You don't need a clean scope to start. You operate well under ambiguity and produce clarity for everyone around you.
You have a hacker mentality - you find the path through, you're comfortable in ambiguous and underdocumented systems, and you debug by instinct as much as by method.
You're technically deep and contextually wide - you can go deep on a data pipeline, a detection rule, a query engine, or a cloud architecture, then explain any of it to an executive the same afternoon.

As a Senior Data Engineer , you will play a key role in building and scaling the companys data infrastructure. Youll be responsible for creating the foundation that enables teams across the organization to access, analyze, and leverage data effectively. Working closely with Data Science, Delivery, Product, GTM, and Analytics teams, youll design and maintain data pipelines, manage our data warehouse, and transform raw data into structured, reliable assets that power business and product decisions.



What Youll Do

Build and Manage Data Infrastructure: Own and maintain Nomas data warehouse infrastructure, ensuring scalability, reliability, and performance
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

Were looking for engineers who combine strong systems skills with product sense: understanding who uses the platform, why certain capabilities matter, and making pragmatic trade-offs to maximize impact. On our team, engineering work is expected to be connected to real users and outcomes - youll regularly align with internal stakeholders, clarify requirements, and help drive prioritization.

In this role, you will:

Design and implement new functionality in YTsaurus core (C++) with production reliability in mind.
Build and evolve platform-level capabilities: platform architecture and operating model-multi-cluster growth, shared primitives, and a consistent experience that scales with new teams and use cases.
Improve end-to-end platform experience for internal (and external-facing) users: APIs, guardrails, debugging workflows, and automation.
Own production quality: incident response / on-call rotation, root cause analysis, and turning learnings into durable fixes.
Example projects
Roll out sharded YTsaurus masters (incl. Kubernetes operator support) and build automatic balancing of metadata across master cells (consensus groups) to remove control-plane bottlenecks and unlock 10-100x cluster growth.
Make CHYT interactive SQL faster and more predictable at high load via performance work like data-skipping / min-max-style indexes and improved execution introspection.
Turn Orchestracto into a platform product by defining the building blocks, developer experience, and governance for how teams create and share workflows.
Scale and harden Parquet-on-S3 for native YTsaurus workloads by tackling replication/movement, consistent lifecycle semantics, and master-server metadata optimizations for performance and reliability.
Design and ship complete, trustworthy audit trails for data changes (who/what/when) across heterogeneous storage and compute paths.
Tech stack
Core: modern C++ (C++20, async + multithreaded primitives)
Services & tooling: Go and Python (microservices, utilities, integration tests)

We are looking for a strong Data Engineer to be part of one of our most strategic initiatives - building the context graph that powers our AI-SOC platform. Our team is building the context layer behind our AI Agents, transforming raw customer data into meaningful, normalized context that enables smarter autonomous security decisions.

This is a unique opportunity to join a brand-new, small, senior team with high ownership, strong product influence, and the chance to build foundational AI infrastructure from the ground up.

What Youll Be Doing:
Think like a product person - collaborate with stakeholders and consumers to design meaningful data models and continuously validate them against real customer data.
Run POCs work end to end - connect a new data source, ingest and normalize it, correlate entities across systems, and demonstrate the value.
Do the analysis that shapes the model - profile new data, measure coverage and accuracy, and find the gaps that matter to the product.
Integrate identity, endpoint, and security data sources - learn how each system exposes its data and turn it into reliable, consistent models.
Build and own scalable data pipelines, transforming raw data into clean, normalized, and well-tested models using Python, SQL, and dbt.

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

We are looking for a hands-on BI Data Developer to design, build, and own the pipelines, models, and governed datasets that power analytics, business decisions, and AI use cases in a fast-moving SaaS environment.

You will own foundational data assets end to end, from ingestion and transformation through modeling, validation, delivery, monitoring, security, and cost optimization. This is a production-focused role where engineers own live systems, not only task-level deliverables.

What will you do:
Build and maintain production-grade Snowflake pipelines using incremental loading, CDC patterns, backfills, reconciliation, and idempotent processing.
Own data flows from Salesforce and other source systems into Snowflake, including ingestion, transformation, validation, monitoring, and delivery.
Model data into analytics-ready datasets, dimensional models, facts, dimensions, star schemas, and reusable business entities.
Develop core Snowflake objects.
Create governed, AI-ready semantic models and curated datasets for Cortex Analyst, Cortex Search, BI tools, and enterprise AI integrations
Apply Snowflake FinOps practices for cost-aware design.
Apply security and governance best practices, including RBAC, least privilege, masking policies, row access policies, secure views, auditability, and safe handling of sensitive data.
Partner with stakeholders to define certified metrics, source-to-target mappings, trusted data contracts, and reusable analytical entities.

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.

As our Senior Data Engineer you will handle Big Data in real time, and become the owner and gatekeeper of all data and data flows within the company. You will bring data ingenuity and technological excellence while gaining a deep business understanding.

This is an amazing opportunity to join a multi-disciplinary A-team while working in a fast-paced, modern cloud, data-oriented environment.

Implement robust, reliable and scalable data pipelines and data architecture.
Own and develop our DWH (Petabytes of data!).
Own the entire data development process, including business knowledge, methodology, quality assurance, and monitoring.
Collaborate with cross-functional teams to define, design, and ship new features.
Continuously discover, evaluate, and implement new technologies to maximize development efficiency.
Develop tailormade solutions as part of our data pipelines.
Lead complex Big Data projects and build data platforms from scratch.
Work on a high-scale, real-time, real-world business-critical data stores and data endpoints.
Implement data profiling to identify anomalies and maintain data integrity.
Work in a results-driven, high-paced, rewarding environment.

Required Data Engineer
About The Position
Design, build, and maintain scalable data pipelines and infrastructure entirely within GCP
Own data solutions end-to-end, from architecture and design through to production delivery
Build and maintain custom connectors to advertising platforms including Google Ads, Meta (Facebook) Marketing API, and other ad platforms
Design and implement data warehouse structures with a deep focus on performance, scalability and usability
Continuously investigate and evaluate new GCP native tools and services, assessing their fit, building proof of concepts, and driving adoption where it makes sense
Set up and configure orchestration and transformation frameworks (such as Airflow and dbt) within the GCP environment
Collaborate closely with backend engineers to ensure data infrastructure aligns with product and system needs
Contribute to data science initiatives and bring genuine curiosity to analytical use cases
Monitor, troubleshoot, and continuously improve the reliability and performance of existing data systems
Define technical standards and best practices for the data platform
Build and maintain data dashboards and reports to surface insights for stakeholders, primarily using Looker Studio.

We seek an experienced and highly skilled Senior Engineer to join our Data Platform Team. This is an exciting opportunity to work on cutting-edge technologies, drive innovation, and play a key role in designing and implementing the companys data infrastructure and architecture.
What you'll do:
Database Infrastructure Development - Design, develop, and maintain scalable and reliable self-serve database infrastructure, including PostgreSQL, MongoDB, SingleStore, and AWS Aurora.
Big Data Infrastructure Development - Design, develop, and maintain scalable and reliable self-serve big data infrastructure, including cloud-agnostic data lakehouse, distributed databases, and platform-powered data pipelines.
End-to-End Ownership - Take responsibility for the entire lifecycle of data infrastructure, from DevOps (Terraform, Kubernetes) to application-level components (infrastructure libraries, core components in data pipelines).
Architecture Leadership: Collaborate with the Architecture group to define and maintain the companys core architectural vision.
Partner with development teams, providing technical guidance, best practices, and support from the design phase to production deployment.
Innovation and Exploration - Work on diverse and impactful projects across different layers of the tech stack, exploring new technologies and approaches to improve reliability, efficiency, and scalability.