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

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:
Minimum 5 years of experience as a Data Engineer mandatory 5 years of experience working with Object-Oriented Programming (OOP) languages mandatory 5 years of hands-on experience with Python mandatory Hands-on experience with Spark for large-scale data processing mandatory At least 2 years of practical experience with AWS , including services such as Athena, Glue, Step Functions, EMR, Redshift, and RDS mandatory
* Deep understanding of design, development, and optimization of complex solutions handling or processing large-scale data
* Familiarity with optimization techniques and working with data partitioning and formats such as Parquet, Avro, HDF5, Delta Lake
* Experience working with Docker, Linux, CI/CD tools, and Kubernetes
* Experience with data pipeline orchestration tools like Airflow or Kubeflow Bachelor’s degree in Computer Science, Engineering, Mathematics, or Statistics – mandatory
* Understanding of machine learning concepts and workflows
* Familiarity with GenAI solutions or prompt engineering advantage

Requirements:
2+ years leading engineers (team lead or manager) building/operating large-scale data platforms; 5+ years total in Data Infrastructure/DataOps roles.
Proven ownership of cloud-native data platforms on AWS: S3, EMR (preferably EMR on EKS), IAM, Glue/Data Catalog, Athena.
Production experience with Apache Iceberg (schema evolution, compaction, retention, metadata ops) and columnar formats (Parquet/Avro).
Hands-on depth in at least two of: Trino/Presto, Apache Spark/Structured Streaming, Apache Druid, Apache Flink.
Strong conceptual understanding of Kubernetes (EKS), including autoscaling, isolation, quotas, and observability
Strong SQL skills and extensive experience with performance tuning, with solid proficiency in Python/Java.
Solid understanding of Kafka concepts, hands-on experience is a plus
Experience running on-call for data platforms and driving measurable SLO-based improvements.
You might also have
Experience building feature platforms (feature definitions, materialization, serving, and online/offline consistency).
Airflow (or similar) at scale; Argo experience is a plus.
Familiarity with BigQuery (and ideally BigLake/Iceberg interop) and operational DBs like Aurora MySQL.
Experience with Clickhouse / Snowflake / Databricks / Starrocks.
FinOps background (cost attribution/showback, Spot strategies).
Data quality, lineage, and cataloging practices in large orgs.
IaC (Terraform/CloudFormation).

Requirements:
Bachelors degree in computer science, Software Engineering, or a related field.
3+ years of experience as a data engineering team leader in a product-based company.
5+ years of hands-on in SQL and Python / Pyspark (or Java / Scala), data modeling techniques and concepts - such as Facts, Dimension, Partitions, etc.
Experience in designing and implementing data ingestion, ETL processes and 3rd party tools, preferably with experience in Data Lakehouse and Data Warehouse architecture
Proven experience with CI/CD processes and agile development methodologies.
Strong interpersonal and communication skills.
Demonstrated ability to manage and develop a team with varying levels of experience.
Skilled in identifying risks, tracking progress, and resolving technical challenges proactively.
Self-motivated and goal-oriented with a high work ethic.
Ability to balance long-term architectural vision with fast-paced delivery, in early-stage development or startup environment.

Requirements:
What you need to succeed:

5+ years of data modeling techniques and concepts - such as Facts, Dimension, Partitions, etc.
5+ years of experience in designing and implementing data ingestion, ETL processes and 3rd party tools, preferably with experience in Data Lakehouse and Data Warehouse architecture
5+ years of hands-on experience in SQL and Python / Pyspark (or Java / Scala)
Data landscape understanding from vendor-specific to open-source (e.g. Snowflake/Redshift, DataBricks, Iceberg, Hadoop, DBT)
A versatile, proactive, team player, can-do attitude, quick learner, and a great executor
How you will stand out from the crowd:
Data Warehouse technologies (especially Iceberg or Snowflake)
AWS ecosystem (Glue, Athena, Kinesis, Redshift, SageMaker, EMR) an advantage
Experience with Data Science (Scikit-learn / TensorFlow / PyTorch, etc.) an advantage
Background in cyber security an advantage
Managerial experience in the data engineering field an advantage.

Requirements:
5+ years of experience in software development, with at least 3 years as a data engineer.
Proven track record designing and implementing scalable ETL/ELT data pipelines.
Strong SQL skills.
Experience with relational and analytical data platforms (e.g., PostgreSQL, Snowflake, data lakes).
Strong coding skills (preferably in Python), with prior software engineering experience (e.g., API development) a plus.
Experience with cloud environments, AWS preferable.

Requirements:
B.Sc. in Computer Science, Electrical Engineering, or related field. (M.Sc. is an advantage)
3+ years of experience in a data engineer/backend/tools dev role, preferably in AI or automotive environments.
Proficiency in Python and data tools (e.g., Pandas, NumPy).
Strong algorithmic thinking and data handling skills.
Familiarity with CV/ML pipelines is a strong plus.
Proficiency in building data queries from scratch.
Hands-on experience with large-scale data (video, sensors, logs, etc) and data visualisation tools.

Requirements:
BSc in EE or Computer Science, Industrial Engineer (or similar field) (MSc advantage)
Know-how in processes and organizational techniques
Experience in heading up exceptionally large-scale business process operations.
Familiar with Data transfer methodologies design and operation
Strong project management skills: experience in working with cross-organizational teams- R&D, IT, Product, and project managers to achieve various project goals in an effective and timed manner.
Ability to handle multiple competing priorities in a fast-paced environment
Experience in big data, database techniques, tagging, and labeling advantage.
Experience as a first-line manager (advantage)
Experience in Computer Vision products advantage
Experience in data architecture, especially from the automotive industry big advantage.

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 WideOps 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:
At least 5 years of experience in backend development/data engineering.
Experience working with SQL and NoSQL databases.
Experience with cloud development.
Working with high scale.
Ability to work in a multi-language environment.

Advantages:
B.Sc. in computer science or equivalent.
Hands-on experience with one or more of the following technologies: NodeJS, .NET Core, Kubernetes, Docker, Airflow, Data Flow and Terraform.
Hands-on experience with multiple services on Google Cloud Platform and/or Firebase.
Proven experience of hardcore performance optimization (tens of milliseconds).
TDD Experience.

Requirements:
Requirements:
Graduate with a degree in Industrial Engineering and Management or a similar major.
Experience with databases and SQL - a Must.
Outstanding technical and analytical skills.
Highly motivated with an exceptional work ethic.
High attention to detail with an ability to work on multiple projects simultaneously.
Strong interpersonal and communication skills.
Quick, self-learning capabilities and creativity in problem-solving.

Preferred:
Familiarity with Python.
Familiarity with Airflow, ETL tools, Snowflake and MSSQL.
Hands-on with VCS (Git).

Requirements:
Bachelor's degree in Computer Science, Engineering, or related field
3+ years experience in data engineering
Strong proficiency in SQL and Python
Experience with ETL tools and data warehousing solutions
Knowledge of big data technologies (Hadoop, Spark, etc.)
Experience with cloud platforms (AWS, Azure, or GCP)
Understanding of data modeling and database design principles
Familiarity with data visualization tools - Tableau, Sisense
Strong problem-solving and analytical skills
Excellent communication and collaboration abilities
Experience with version control systems (Git).

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.