לוח משרות דרושים מהנדס/ת דאטה/DATA ENGINEER משרה מלאה

Requirements:
Qualifications:
At least 5 years of experience in Big-Data technologies as a data engineer, including ETL /ELT processes and data exploration.
At least 3 years of experience in building end-to-end data pipelines using Spark, Databricks or similar tools is required.
At least 3 years of experience in Python programming and SQL queries. Experience as a backend Developer - advantage.
Team player committed to the success of the team.
Strong verbal and written communication skills with the ability to clearly explain technical concepts.
Excellent debugging, investigating and problem-solving abilities.
Curious learner who loves sharing knowledge and best practices, and can work both independently and in a team.
Ability to thrive in a fast-paced, ambiguous, and changing environment.

Requirements:
Requirements:
3+ years of experience in data engineering or a related field.​
Proficiency in SQL and experience with modern lakehouse modeling
Hands-on experience with data pipeline orchestration tools like Apache Airflow.​
Experience with DBT for data transformation and modeling.​
Familiarity with data visualization tools such as Tableau.​
Strong programming skills in languages such as Python or Java.​
Hands-on experience with AWS data solutions (or other major cloud vendor)
Excellent problem-solving skills and attention to detail.​
Strong communication skills and the ability to work collaboratively in a team environment.​
Relevant academic degree in Computer Science, Engineering, or related field (or equivalent work experience).

Preferred Qualifications:
Experience in the travel or insurance industries.​
Familiarity with Mixpanel or similar analytics platforms.​
Knowledge of data security and privacy best practices.

Requirements:
BSc/BA in Computer Science or similar.
At least 6 years proven experience as a Big Data Engineer.
At least 3 years of experience with Python.
Experience with both SQL and NoSQL databases, including Elastic Search, Splunk, MongoDB.
Experience with processing of large data sets.
Experience with Linux environment.
Experience with software design and development in a test-driven environment.

Requirements:
6+ years of experience building and maintaining production-grade ETL pipelines.
Hands-on experience with orchestration tools such as Databricks, Airflow, dbt, or similar.
Proven ability to design systems that support diverse data consumers with varying SLAs.
Deep understanding of data modeling, distributed systems, and cloud infrastructure.
Strong background in Apache Spark (PySpark or Scala).
Familiarity with microservices architectures and clean API/data contracts.
Excellent communication and collaboration skills youre proactive, approachable, and solution-oriented.
Ability to think in systems: conceptualize high-level architecture and break it into components.
Nice to Have:
Knowledge of data governance, lineage, and observability best practices.
Experience with real-time streaming technologies (e.g., Kafka, Flink).
Exposure to DevOps practices for data systems, including CI/CD, monitoring, and infrastructure-as-code.
Previous experience developing customer-facing data products or analytics tools.

Requirements:
13 years of experience in a data-related role (data engineering, analytics engineering, BI) or strong projects/coursework if you're just starting out.
Strong experience with SQL and Python for building, manipulating, and analyzing data
Comfortable with modern data tooling such us - Snowflake, dbt, Airflow, or similar
Enthusiastic about working collaboratively with teammates and stakeholders to deliver business value from data
Strong communicator and continuous learner, ready to tackle new challenges in a fast-paced environment
Hands-on experience with cloud platforms such as AWS, GCP, or Azure, and familiarity with services like AWS Glue, Google BigQuery, or Azure Data Factory.
Hands-on experience with ETL/ELT processes, data ingestion, data transformation, data modeling, and monitoring.
Nice to Have:
Experience with AWS or other cloud platforms.
Familiarity with streaming data (Kafka), Infrastructure as Code (Terraform), or Git-based workflows
Knowledge of SaaS analytics, especially for product or customer behavior.
Understanding of PII, data privacy, or compliance standards.

Requirements:
Requirements:
4+ years of experience as a Software Engineer, including design & development.
Proven experience with Java or Go.
Experience in the design and development of scalable big data solutions.
Experience working in a cloud-based environment.
Passionate about technologies, frameworks, and best practices.
Ability to work in a fast-paced environment.

Advantages:
Experience with Spring / Kubernetes.
Experience with Terraform / Helm / Argo.

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:
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.

Requirements:
Have 5+ years of hands-on experience in designing and maintaining big data pipelines across on-premises or hybrid cloud environments, with proficiency in both SQL and NoSQL databases within a SaaS framework.
Proficient in one or more programming languages: Python, Scala, Java, or Go.
Experienced with software engineering best practices and automation, including testing, code reviews, design documentation, and CI/CD.
Experienced in building and designing ML/AI-driven production infrastructures and pipelines.
Experienced in developing data pipelines and maintaining data lakes on AWS - big advantage.
Familiar with technologies such as Kafka, Snowflake, MongoDB, Airflow, Docker, Kubernetes (K8S), and Terraform - advantage.
Bachelor's degree in Computer Science or equivalent experience.
Strong communication skills, fluent in English, both written and verbal.
A great team player with a can-do approach.