לוח משרות דרושים ראש צוות דאטה

Requirements:
Minimum 5 years of hands-on experience designing, building, and maintaining large-scale data pipelines for both batch processing and streaming use cases.
Deep expertise in Python and SQL, with a focus on writing clean, performant, and maintainable code.
Strong analytical and problem-solving skills, with the ability to break down complex technical challenges and align solutions to business objectives.
Solid background in data modeling, analytics, and designing architectures for scalability, performance, and cost efficiency.
Practical experience working with modern OLAP systems and cloud data platforms, including Databricks, Snowflake, or BigQuery.
Familiarity with AI agent protocols (such as A2A, MCP) and LLM-related technologies (e.g., vector databases, embeddings) is a plus.
AI-savvy, with comfort adopting AI tools and staying current with emerging AI trends and technologies.

Requirements:
* Hands-on experience in system architecture with a focus on cloud, especially Google Cloud.
* Experience in writing system and data architecture documentation.
* Experience in cloud migration and building machine learning models in Python.
* Experience in container development.
* Leadership experience and the ability to manage tasks.
* Excellent communication skills, both written and verbal.
* Proactive, highly motivated, and hands-on approach.

Requirements:
A Masters or Ph.D. in Computer Science, Machine Learning, or a related field.
Over 5 years of experience in AI/ML roles, with at least 1 year in a leadership capacity.
A strong technical background in machine learning, deep learning, and data science.
A proven track record of successfully delivering AI projects in a production environment.
Expertise in Python and popular ML frameworks.
Experience with cloud platforms (AWS, GCP, or Azure) and proficiency in MLOps tools is a plus.
A strong understanding of software engineering principles and best practices.
Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical audiences.
Familiarity with agile development methodologies.
Strong leadership and mentoring abilities, with the talent to inspire and motivate a team.

Requirements:
3+ years experience as team lead
5+ years experience as a Data Scientist
Proven experience delivering to production AI/ML systems (LLMs, conversational AI, or agent-based systems preferred), including concepts such as prompt engineering, agent orchestration, and RAG
Proven Experience with model evaluation, A/B testing, monitoring frameworks
Experience leading DS, Analytics & AI engineers in cross functional teams
Strong SE skills & experience in developing production level Python code
Ability to work across disciplines (Product, Engineering, Data) and communicate technical ideas clearly
Preferred Qualifications:
Experience in clinical, healthcare, or regulated environments
Experience delivering agents using LangGraph/LangChain

Requirements:
1. Technical Expertise
Cloud Data Platforms: Expertise in Google Cloud Platform (GCP)
services, especially in data-related services like BigQuery, Cloud SQL,
Dataproc, Pub/Sub, and Dataflow.
Data Engineering: Strong knowledge of data pipelines, ETL/ELT
processes, and data integration using GCP tools.
Database Management: Experience with both relational and
non-relational databases, including MySQL, PostgreSQL, and NoSQL
solutions like Firestore or Bigtable.
Programming Skills: Proficiency in Python, SQL, and possibly other
languages like Java or Scala, with a focus on data manipulation and
processing.
APIs and Integrations: Experience working with Google APIs and
third-party APIs for data extraction and integration.
Machine Learning: Familiarity with Google Cloud AI tools, such as
Vertex AI, and how to integrate machine learning workflows into the data
architecture.
Data Governance: Knowledge of data security, privacy, governance, and
regulatory requirements (GDPR, HIPAA, etc.).
2. Leadership and Management

Team Management: Ability to lead a team of data engineers, analysts,
and architects, providing guidance, mentorship, and performance
management.
Project Management: Strong skills in managing data projects, ensuring
timely delivery while meeting client requirements.
Collaboration: Experience working cross-functionally with stakeholders
such as solution architects, developers, and business teams to translate
business needs into data solutions.
Resource Allocation: Efficiently managing cloud resources to optimize
cost and performance.
3. Client-Facing Skills
Consulting: Experience in providing consulting services to clients,
including understanding their data needs, providing solutions, and
guiding them through the data modernization process.
Pre-sales Support: Supporting sales teams in scoping out client
projects, preparing presentations, and explaining technical aspects of
data solutions during pre-sales.
Client Relationship Management: Building and maintaining strong
relationships with clients to ensure satisfaction and long-term
partnership.
4. Certifications and Education
● Google Cloud Certifications:
● Professional Data Engineer (preferred)
● Professional Cloud Architect (preferred)
● Other Certifications: Optional certifications in data engineering or
machine learning from organizations like Cloudera, AWS, or Microsoft
Azure can be advantageous.
● Educational Background: Bachelors or Masters degree in Computer
Science, Data Science, Engineering, or a related field.
5. Soft Skills
● Problem-Solving: Ability to troubleshoot data-related issues and
implement scalable solutions.
● Adaptability: Ability to keep up with the rapidly changing cloud and data
landscape.
● Communication: Excellent verbal and written communication skills to
explain complex data systems to non-technical stakeholders.