לוח משרות דרושים מהנדס/ת דאטה/DATA ENGINEER עבודה היברידית, לחיפוש זה נמצאו 16 משרות

Requirements:
Required Skills and Experience
Bachelors degree or higher in Computer Science, Software Engineering, EE, Information Systems, or a related field
Minimum 8+ years of experience as a Dataflow and DevOps development, Backend Engineer, Data Engineer, or similar role
Strong experience with Python
Strong SQL skills, including query optimization, indexing, stored procedures, and performance tuning
Experience with ETL/ELT processes, data pipelines, and integration between systems
Strong experience with automation scripting language (ex: Groovy), data processing, and tooling
Experience with Docker, Kubernetes, Jenkins and CI/CD frameworks
Familiar with developing in Linux and Windows environments
Experience with backup, restore, migration, monitoring, and database maintenance processes
Understanding of data security, permissions, access control, and data integrity practices
Experience with Git, Jira, and Agile development workflows
High English skills: listening, speaking, and reading (C1-2)
Nice to have:
Past experience with CloudBees, Antifactory, and integrating them into a secure system
Strong experience with relational databases such as PostgreSQL and Pgvector, MySQL, and NoSQL
Experience with cloud database services such as AWS RDS, Azure SQL, Google Cloud SQL, or similar
Experience with database schema design, data modelling, and database normalization
Experience with AI agents and AI automation
Familiarity with database replication, clustering, high availability, and disaster recovery
Experience with BI/reporting tools or data visualization platforms
Experience with backup solutions and backup strategies
Experience working in an R&D, semiconductor, embedded systems, or hardware/software engineering environment
Experience and background in MAL, datasets, and MAL Python libraries.
Experience in JAVA.

Requirements:
3+ years of data engineering management leading architecture and strategy
6+ years of experience building large-scale (at least 5 million events a day) distributed systems in Data Engineering, ML Engineering, or Software Engineering roles.
Proven ownership of production-grade data or ML platforms, including delivery and adoption across R&D and Product stakeholders.
Hands-on experience building and operating high-scale distributed data systems (Spark, Storm, Flink) in production.
Strong experience with Java and Python in AWS cloud environments.
Advantages:
Proven track record leading multi-disciplinary teams and driving measurable business impact through data/ML systems.
Experience building ML platforms, feature stores, or self-serve data infrastructure at scale.
Deep experience with modern ML/infra stack (PyTorch, TensorFlow, SageMaker, Kubernetes, Argo).
Experience with modern data lakehouse and analytics stack (Iceberg, Athena, ClickHouse, data catalogs, data quality frameworks).
Experience deploying LLM-based systems or AI-driven infrastructure in production environments.

Requirements:
At least 6 years of production software engineering experience, primarily in backend systems, including meaningful ownership of data-intensive platforms or infrastructure.
Strong proficiency in Python, Java, and SQL, with experience in testing, typing, performance optimization, and production debugging.
Strong backend engineering fundamentals, including service and API design, asynchronous and concurrent processing, failure handling, and operational reliability.
Experience with relational and non-relational databases such as PostgreSQL, Redis, or MongoDB.
Hands-on experience with AWS, Docker, Kubernetes, CI/CD, and production observability.
Deep experience designing and operating ETL/ELT pipelines and batch or streaming data systems.
Hands-on production experience with Apache Flink for distributed stream processing.
Hands-on production experience building reactive data-processing pipelines with RxJava/Project Reactor, including backpressure, scheduling, error handling, testing, and operational debugging.
Experience with event-driven architectures and messaging systems such as Kafka or AWS SQS.
Strong understanding of data modeling, warehouse and lakehouse architecture, schema evolution, and data quality.
Hands-on experience with dbt for data modeling, testing, documentation, lineage, and semantic-layer development.
Hands-on experience with at least one analytical data platform such as Snowflake, Databricks, ClickHouse, Athena, Trino, or Dremio.
Hands-on experience with at least one dataframe library: Pandas, Polars, or Daft.
Experience with workflow orchestration tools such as Airflow or Dagster.
Clear communication skills and a strong ownership mindset, with a track record of leading cross-functional technical initiatives through to production.