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:
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:
3+ years experience in data engineering or platform engineering roles
Strong programming skills in Python and SQL
Experience with orchestration platforms like Airflow/Dagster/Temporal
Experience with MPPs like Snowflake/Redshift/Databricks
Hands-on experience with cloud platforms (AWS) and their data services
Understanding of data modeling, data warehousing, and data lake concepts
Ability to optimize data infrastructure for performance and reliability
Experience working with containerization (Docker) in Kubernetes environments.
Familiarity with CI/CD concepts
Fluent in English, both written and verbal
And it would be great if you have (optional):
Experience with big data processing frameworks (Apache Spark, Hadoop)
Experience with stream processing technologies (Flink, Kafka, Kinesis)
Knowledge of infrastructure as code (Terraform)
Experience building analytics platforms
Experience building clickstream pipelies
Familiarity with machine learning workflows and MLOps
Experience working in a startup environment or fintech industry

Requirements:
3+ years experience in data engineering or platform engineering roles
Strong programming skills in Python and SQL
Experience with orchestration platforms like Airflow/Dagster/Temporal
Experience with MPPs like Snowflake/Redshift/Databricks
Hands-on experience with cloud platforms (AWS) and their data services
Understanding of data modeling, data warehousing, and data lake concepts
Ability to optimize data infrastructure for performance and reliability
Experience working with containerization (Docker) in Kubernetes environments
Familiarity with CI/CD concepts
Fluent in English, both written and verbal
And it would be great if you have (optional):
Experience with big data processing frameworks (Apache Spark, Hadoop)
Experience with stream processing technologies (Flink, Kafka, Kinesis)
Knowledge of infrastructure as code (Terraform)
Experience building analytics platforms
Experience building clickstream pipelines
Familiarity with machine learning workflows and MLOps
Experience working in a startup environment or fintech industry.