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

Requirements:
Proven hands-on experience with Drools. Strong experience with DMN and Decision Tables. Experience designing and implementing complex business rules. Strong understanding of business decision management. Ability to separate business logic from application code. Experience working in complex Enterprise environments. Banking / financial services experience - a strong advantage.

Requirements:
6+ years of software engineering experience building production-level systems.
2+ years of engineering management or technical leadership experience.
Strong experience building large-scale backend systems in Python.
Experience developing modern web applications using frameworks such as React / Next / Angular / Vue.
Experience deploying and operating LLM-based systems in production, including evaluation and iteration.
Strong understanding of data pipelines, experimentation, and product analytics.
Experience with modern cloud environments such as Google Cloud Platform or Amazon Web Services.
Passion for clean code, scalable architectures, and data-driven product development.
Experience with prompt engineering, RAG architectures, and vector databases.
Experience building AI agents or autonomous workflows
Nice to Have:
Experience with frameworks such as ADK, A2A, LangChain, LangGraph, or LlamaIndex or equivalent
Experience with gRPC and protobuf-based architectures
Experience building MCP servers
Background in data engineering, experimentation platforms, or ML infrastructure.

Requirements:
Excellent communication and collaboration skills
English at a high level, written and spoken required
Ability to work from our Jerusalem office (located in the Central Bus Station next to the train) 2 times a week (Monday & Wednesday) is required
Strong attention to detail, ownership mentality, and ability to work independently
Quick learner who can dive into new codebases, technologies, and systems independently
Hands-on mentality - not afraid to roll up your sleeves, dig into unfamiliar code, and work across the stack (including backend when needed)
4+ years of experience as a Data Engineer
Strong problem-solving skills for complex data challenges at scale - ability to debug performance issues, data inconsistencies, and system bottlenecks in high-volume environments
Experience with data modeling and schema design for analytical workloads
Strong proficiency in SQL and experience with complex analytical queries
Hands-on experience building and maintaining data pipelines (ETL/ELT)
Ability to troubleshoot and optimize systems handling large data volumes (millions+ rows, complex queries, high throughput)
Knowledge of query optimization techniques and execution planning
Familiarity with columnar databases (ClickHouse, BigQuery, Redshift, Snowflake, or similar). Columnar DB experience is a big plus.
Nice to Have
Experience with ClickHouse specifically
Experience with real-time or streaming data pipelines
Familiarity with SQL compilers or query engines
Background in data quality frameworks and observability tools
Experience with infrastructure as code (Terraform, Ansible, Pulumi ,etc.)
Contributions to open-source data projects
Experience with data orchestration tools (Airflow, Dagster, Prefect, etc.)
Experience with any scripting language for data processing
Understanding of distributed systems and data architecture concepts at scale

Requirements:
Requirements:
5+ years of hands-on experience building production data solutions for SaaS organizations on Snowflake as a Data Engineer, BI Developer, Analytics Engineer, or similar role.
Strong experience with Snowflake objects and capabilities, including warehouses, databases, roles, schemas, views, stored procedures, tasks, streams, and performance tuning.
Experience building reliable ELT or ETL pipelines using incremental loads, CDC, backfills, reconciliation, and idempotent processing.
Strong dimensional modeling and data warehousing foundations, including facts, dimensions, star schemas, marts, and reusable business entities.
Salesforce data experience, including core objects, relationships, API limitations, and Salesforce-to-warehouse pipelines.
Hands-on experience with Snowflake Cortex capabilities such as Cortex Analyst, Cortex Search, Cortex LLM functions, and governed AI data access patterns.
Experience building AI-ready semantic models and curated datasets for governed analytics, BI, and enterprise AI use cases.
Understanding of secure enterprise AI integration patterns, including governed structured data access and MCP-based integrations.
Strong knowledge of data governance, privacy, security, RBAC, masking policies, row access policies, secure views, and compliance-aware data handling.
Proven Snowflake FinOps experience, including credit monitoring, warehouse sizing, workload optimization, storage awareness, and query cost reduction.
Strong system analysis, requirements gathering, source-to-target mapping, lineage, impact analysis, and stakeholder communication skills.

Nice to have:
Python for data processing, automation, testing, or operational tooling.
AWS cloud data experience.
Experience with Snowpark or Snowpark ML.
Experience with BI tools such as Sisense, Tableau, or Power BI.
Experience with data observability, orchestration, CI/CD, or automated testing for data pipelines.

Requirements:
Requirements: must have requirements: • Bachelor’s degree in Computer Science, Data Science, Statistics, or another relevant field, or equivalent professional experience. • At least 5 years of experience in Data Engineering, with a focus on Analytics and BI. • Expert-level SQL skills. • Deep experience with PostgreSQL, including performance tuning and optimization. • At least 3 years of experience with Python for data processing, automation, and testing. • At least 3 years of experience designing and implementing large-scale data models. • Experience leading performance optimization in complex data systems. • Experience establishing and implementing Data Governance and Data Quality processes. • Proven ability to translate business requirements into data architecture and technological solutions. • Experience providing technical leadership, mentoring, and leading technological initiatives. • Experience building Data and Analytics platforms from scratch. • Proven experience integrating AI and LLM tools into development and data engineering processes. Advantages: • Experience in FinTech or financial organizations. • Deep familiarity with regulation, information security, and compliance requirements. • Experience working with Data Lakes and Data Warehouses. • Experience with orchestration tools and ELT / ETL processes. • Experience working in cloud environments such as AWS, GCP, or Azure. • Familiarity with streaming technologies and real-time data processing. • Experience leading technology teams or professional excellence groups. Personality requirements: • Fast learning ability and curiosity for new technologies and methodologies. • Strategic thinking and broad system-wide perspective. • Ability to lead and influence without formal authority. • Ability to work independently and manage multiple tasks simultaneously. • High personal responsibility and ability to receive professional feedback. • Initiative, creativity, and ability to solve complex problems. • High motivation and constant drive for excellence. • Excellent interpersonal communication skills and ability to work with multiple stakeholders. • Ability to driv

Requirements:
3+ years of engineering management experience leading Data / ML / Software engineering teams in production environments.
6+ years of experience building large-scale 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.