דרושים » דאטה » data Engineer לחברה גדולה בצפון!

Requirements:
What we need to see:

B.Sc. or M.Sc. in Computer Science, Computer Engineering, or a related field.

5+ years of hands-on experience in data engineering or backend development.

Strong practical experience with Apache Spark (PySpark or Scala) and Databricks.

Expertise with Apache Kafka, including stream ingestion, schema registry, and event processing.

Proficiency in Python and SQL for data transformation, automation, and pipeline logic.

Familiarity with ETL orchestration tools (Airflow, Prefect, or Dagster).

Experience with schema evolution, data versioning, and validation frameworks (Delta Lake, Iceberg, or Great Expectations).

Solid understanding of cloud environments (AWS preferred; GCP or Azure also relevant).

Knowledge of streaming and telemetry data architectures in large-scale, distributed systems.

Ways to stand out from the crowd:

Exposure to hardware, firmware, or embedded telemetry environments.

Experience with real-time analytics frameworks (Spark Structured Streaming, Flink, Kafka Streams).

Experience with data cataloging or governance tools (DataHub, Collibra, or Alation).

Familiarity with CI/CD for data pipelines and infrastructure-as-code (Terraform, GitHub Actions).

Experience designing performance metrics data systems (latency, throughput, resource utilization) that support high-volume, high-frequency telemetry at scale.

Requirements:
What we need to see:
Bachelors or masters degree in computer science, Engineering, or a related field.
5+ years of relevant experience.
Demonstrated track record as a hands-on Data Engineer, driving the successful delivery of sophisticated data projects. Your experience speaks volumes.
Experience with cloud-based data platforms like AWS, Azure, or GCP. You harness the power of the cloud to unlock data's full potential.
Strong programming skills in languages such as Python or Scala, with experience in building scalable and efficient systems.
Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
Solid knowledge in Linux environment.

Ways to stand out from the crowd:
Attention to detail is unwavering. You take pride in delivering data of the highest quality, turning it into insights that power critical business decisions.
Experience working and data engineering frameworks such as Kafka, Cloudera, Spark, Airflow or Hadoop.
Growing in a multifaceted environment, able to balance multiple priorities admirably without compromising on quality or precision.

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:
What we need to see:
Bachelors or masters degree in computer science, Engineering, or a related field.
5+ years of relevant experience.
Demonstrated track record as a hands-on Data Engineer, driving the successful delivery of sophisticated data projects. Your experience speaks volumes.
Experience with cloud-based data platforms like AWS, Azure, or GCP. You harness the power of the cloud to unlock data's full potential.
Strong programming skills in languages such as Python or Scala, with experience in building scalable and efficient systems.
Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
Solid knowledge in Linux environment.

Ways to stand out from the crowd:
Attention to detail is unwavering. You take pride in delivering data of the highest quality, turning it into insights that power critical business decisions.
Experience working and data engineering frameworks such as Kafka, Cloudera, Spark, Airflow or Hadoop.
Growing in a multifaceted environment, able to balance multiple priorities admirably without compromising on quality or precision.