לוח משרות דרושים הייטק דאטה משרה מלאה

Requirements:
6+ years of hands-on experience delivering production-grade data science and machine learning projects.
Deep expertise in probability, statistics, and machine learning, with a proven ability to select, adapt, and apply advanced algorithms to real-world problems.
Proven deep learning experience, including model design, training, and evaluation.
Strong proficiency in Python with solid working knowledge of SQL.
Advanced degree (MSc or PhD) in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a closely related field.
Demonstrated ownership of end-to-end research POCs, from problem formulation and experimental design through execution, analysis, and final recommendations.
Strong emphasis on engineering-quality, production-ready code with high standards for reliability, scalability, and maintainability.
Excellent communication skills, with the ability to clearly articulate complex technical insights to both technical and business stakeholders.
Advantages:
Background in the cybersecurity domain, with focus on endpoint security
Experience working with big data platforms (e.g., GCP).
Familiarity with cloud-native architectures and services for data processing.

Requirements:
5+ years of hands-on experience in data engineering, with at least 2 years in a team or technical leadership role.
Strong technical background in building and maintaining production-grade data pipelines using tools like Airflow, Spark, Kafka, or similar.
Excellent communication skills, with the ability to clearly convey technical ideas to both technical and non-technical stakeholders.
Strong cross-functional collaboration skills, including working directly with product managers on sprint planning and aligning engineering with business priorities.
Proven experience working with both structured and unstructured data, including RDBMS and NoSQL solutions.
Deep understanding of cloud-based data platforms (preferably AWS) and infrastructure as code.

Nice to have:
Familiarity with ML pipelines, feature stores, and supporting data scientists in production environments.
Familiarity with Generative AI tools such as Cursor, GitHub Copilot, Claude Code or similar technologies.
Strong grasp of data security, reliability, and compliance practices.
Exposure to real-time or near-real-time data systems in mobility, IoT, or similar domains.

Requirements:
Minimum 3 years of managing or leading ML or Data Science teams
Strong programming skills and a good understanding of software engineering principles and clean code practices.
Expert-level knowledge of Python for machine learning and data manipulation (pandas, NumPy).
Advanced experience with SQL for data querying and manipulation.
Experience with Git, Bash, Docker, and machine learning pipelines.
Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy.
Hands-on experience working with multi-modal data (images, text) and relevant ML techniques.
Experience with cloud technologies and data storage solutions, including Snowflake.

Requirements:
B.Sc. in Computer Science or equivalent.
7+ years of extensive SQL experience (preferably working in a production environment)
Experience with programming languages (preferably, Python) a must!
Experience with "Big Data" environments, tools, and data modeling (preferably in a production environment).
Strong capability in schema design and data modeling.
Understanding of micro-services architecture.
Familiarity with Airflow, ETL tools, Snowflake, and MSSQL.
Quick, self-learning and good problem-solving capabilities.
Good communication skills and collaborative.
Process and detailed oriented.
Passion to solve complex data problems.

Requirements:
MSc in Computer Science/Engineering or related field with a focus on applied statistics, AI, machine learning, or related fields - a must.
6+ years of production environment experience, working with predictive and probabilistic models, clustering algorithms, classification models, search and recommendation techniques - a must.
Proven record for successful production implementation of translating product/business requirements into a technical solution.
Experience with Large Language Models (LLMs), including fine-tuning, prompt/context engineering, and working with modern AI/LLM toolchains.
Team player, Ability to handle several tasks simultaneously.
Strong knowledge in Python, SQL, and NoSQL.
Spark and Big data technologies experience an advantage.
Research experience - an advantage.
Experience with e-commerce - an advantage.

Requirements:
High proficiency in SQL - a must
At least 4 years of experience as a Data analyst (Online industry- advantage)- must
BA/B.Sc. in industrial/information systems engineering, computer science, statistics, financial or equivalent
Experience in complex data analysis with actionable insights
Experience with reporting tools (Tableau advantage)
Experience in working with Big data (BigQuery advantage).

Requirements:
3+ years of experience as a Data Analyst (online industry advantage).
B.Sc. in Industrial Engineering, Information Systems, Computer Science, Statistics, or equivalent must.
Strong SQL skills must; BigQuery advantage.
Experience with Tableau must.
Experience with AB test methodologies - a must
Strong analytical and problem-solving skills.
Excellent communication and collaboration skills.

Requirements:
Must Haves:
5+ years in data engineering with strong Python & software engineering skills
Extensive experience with cloud platforms (AWS, GCP, Azure) and cloud-native architectures
Deep knowledge of relational (PostgreSQL) and NoSQL databases
Experience designing and implementing data warehouses and data lakes
Strong understanding of data modeling techniques
Expertise in data manipulation libraries (e.g., Pandas) and big data frameworks
Experience with data validation tools (Pydantic, Great Expectations)
Proficiency in writing and maintaining unit and integration tests (e.g., Pytest)
Nice to Haves / Advantage:
Apache Iceberg, Apache Spark, or modern data stack tools (DBT, DuckDB, Dagster, Airflow, Prefect)
Backend system design: RESTful APIs, microservices, event-driven systems, RabbitMQ, Kafka
Containerization & IaC: Docker, Kubernetes, Terraform
Stream processing: Kafka, Flink
Knowledge of compliance (GDPR, CCPA)
Mentoring or leading small teams
Excellent communication and ability to explain technical concepts clearly
Relevant cloud or data certifications.

Requirements:
Have 5+ years of experience as a BI Developer or Data Engineer including ETL design, implementation, optimization and maintenance.
Very strong understanding of business processes and the ability to translate business needs into relevant data products.
An independent, self-motivated learner with a strong passion for data.
Experienced in writing complex SQL queries, Data Modeling and working with DBT.
Experience analyzing user behavior and modeling data.
Proven track record of merging multiple data sources to develop a single source of truth for reporting/analysis/research needs.
Experience preparing data for business intelligence tools such as Tableau, Looker, or similar platforms.
Bachelors or Masters Degree in Computer Science, Information Systems or a related field.

Requirements:
Demonstrated ability to deliver impactful analytics and insights in complex, fast-paced environments
Experience in analyzing large datasets, identifying trends, and drawing actionable conclusions
A solid understanding of fraud prevention, and AI-driven risks in the current industry landscape
Comfortable working independently while collaborating effectively across internal teams to drive alignment and solutions
Strong communication skills, with the ability to articulate complex findings to technical and non-technical stakeholders alike
Attention to detail and a strategic mindset, capable of thinking beyond immediate tasks to the broader goals of trust and safety.

Requirements:
Proven experience as a Solution Architect or in a similar role, with a strong focus on DataOps.
Deep understanding of data management principles, data modeling, and ETL processes.
Expertise in cloud technologies (AWS, Azure, GCP) and data platforms (Hadoop, Spark, Snowflake, Looker).
Strong knowledge of programming languages (Python, Java, Scala) and scripting languages (Bash, PowerShell).
Experience with DevOps tools and practices (CI/CD, containerization, orchestration).
Excellent communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.
Strong problem-solving and analytical skills
Bachelors degree in Computer Science, Engineering, or a related field.
Additional Info:
Send us your CV in English and become part of our team!
Only short-listed candidates will be contacted.
Confidentiality of all applications is assured!

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.

Requirements:
Requirements
3+ years of experience in data engineering or a related role within a production environment.
Proficiency in Python and SQL
Experience with both relational (e.g., PostgreSQL) and NoSQL databases (e.g., MongoDB, Elasticsearch).
Familiarity with big data AWS tools and frameworks such as Glue, EMR, Kinesis etc.
Experience with containerization tools like Docker and Kubernetes.
Strong understanding of data warehousing concepts and data modeling.
Excellent problem-solving skills and attention to detail.
Strong communication skills, with the ability to work collaboratively in a team environment.

Preferred Qualifications:
Experience with machine learning model deployment and MLOps practices.
Knowledge of data visualization tools and techniques.
Practical experience democratizing the companys data to enhance decision making.
Bachelors degree in Computer Science, Engineering, or a related field.

Requirements:
Deep understanding of AI technologies, including large language models
Expertise in prompt engineering and AI-powered automation
Proficiency with AI tools such as ChatGPT, Claude, Midjourney, and Copilot
Knowledge of AI ethics and regulatory considerations
Strong problem-solving and analytical skills
Proficiency with Python or TypeScript for building AI workflows and data pipelines
Excellent communication and leadership abilities
Ability to translate complex AI concepts for non-technical audiences
Experience in project management and cross-functional collaboration
You might also have
Advanced degree in Computer Science, AI, or related field
Previous experience in AI implementation within an organizational setting
Certifications in relevant AI technologies or platforms
Familiarity with no-code AI application development
Bachelor's degree in Computer Science, AI, or related field.