לוח משרות דרושים הייטק דאטה עבודה היברידית

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:
4+ years of experience in data engineering, including cloud-based data solutions.
Proven expertise in implementing large-scale data solutions.
Proficiency in Python, SQL, PySpark is a plus.
Experience with ETL processes.
Experience with cloud and technologies such as Databricks (Apache Spark) and Azure Data factory.
Strong analytical and problem-solving skills, with the ability to evaluate and interpret complex data.
Experience leading and designing data solutions end-to-end, integrating with multiple teams, and driving tasks to completion.
Advantages :
Familiarity with either On-premise or Cloud storage systems
Excellent communication and collaboration skills, with the ability to work effectively in a multidisciplinary team.

Requirements:
6+ years of hands-on experience in Machine Learning, Data Science, or related technical roles in the industry
Entrepreneurial mindset: Excited about building something new and comfortable with the ambiguity of a startup-like environment within a larger company
Proven track record of building and deploying machine learning models in production environments
Technology leadership experience: History of making technical stack decisions and establishing development practices
Strong proficiency in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and programming languages (Python, SQL)
Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools and practices
Team building skills: Ability to establish technical culture and practices that will scale with team growth
Excellent communication skills with ability to explain technical concepts to stakeholders and influence technical decisions
Self-starter mentality: Comfortable taking ownership, making decisions, and driving initiatives independently
Master's degree in Computer Science, AI, Data Science, Mathematics, or related quantitative field- PhD is a plus
Experience in fintech or financial services is a plus
Bonus: Experience being a founding engineer or early hire in a technical team.

Requirements:
You should apply if you are:
A data professional with 4+ years of experience in analytics engineering, BI development, or similar data roles.
Highly skilled in SQL, with hands-on experience using Snowflake or similar cloud data warehouses.
Proficient in DBT for data transformation, modeling, and documentation.
Experienced with Tableau or similar BI tools for data visualization.
Familiar with CI/CD for data workflows, version control systems (e.g., Git), and testing frameworks.
A strong communicator who can collaborate effectively with both technical and non-technical stakeholders.
Holding a B.Sc. in Industrial Engineering, Computer Science, or a related technical field.
Passionate about translating complex data into clear, scalable insights that drive product innovation.
Bonus points if you have:
Experience with event instrumentation and user behavioral data.
Scripting ability in Python for automation and data processing.
Familiarity with modern data stack tools such as Airflow, Fivetran, Looker, or Segment.