דרושים » דאטה » data Engineer

Requirements:
3+ years of hands-on experience designing and implementing large-scale, server-side data solutions
4+ years of programming experience, preferably in Python and SQL, with a strong understanding of data structures and algorithms
Proven experience in building algorithmic solutions, data mining, and applying analytical methodologies to optimize data processing and insights
Proficiency with orchestration tools such as Airflow, Kubernetes, and Docker Swarm, ensuring seamless workflow automation
Experience working with Data Lakes and Apache Spark for processing large-scale datasets strong advantage
Familiarity with AWS services (S3, Glue, EMR, Redshift) nice to have
Knowledge of tools such as Kafka, Databricks, and Jenkins a plus
Strong command of a variety of storage engines, including Relational (PostgreSQL, MySQL), Document-based (MongoDB), Time-series / Search (ClickHouse, Elasticsearch), Key-value (Redis)
Comfortable working with AI tools and staying ahead of emerging technologies and trends

Requirements:
5+ years of experience as a Software Engineer with focus on data engineering
Expert knowledge in building and maintaining data pipelines at scale
Strong experience with stream/batch processing frameworks (e.g. Apache Spark, Flink)
Profound understanding of message brokers (e.g. Kafka, RabbitMQ)
Experience with data warehousing and lake technologies
Strong Python programming skills and experience building data engineering tools
Experience with designing and maintaining Python SDKs
Proficiency in Java for data processing applications
Understanding of data modeling and optimization techniques
Bonus Points
Experience with ML model deployment and maintenance in production
Knowledge of data governance and compliance requirements
Experience with real-time analytics and processing
Understanding of distributed systems and cloud architectures
Experience with data visualization and lineage tools/frameworks and techniques.

Requirements:
5+ years of experience in data engineering & Big Data Analytics.
Data Engineering & Automation: Building robust, production-ready data pipelines using SQL, Python, and PySpark, while managing ETL workflows and orchestrating data processes with Airflow (unmanaged) and Databricks.
Big Data Analysis & Distributed Processing: Expertise in Databricks (Spark, etc.) for handling large-scale data analytics with optimized efficiency.
Cloud Infrastructure: Proficient in Cloud Services (preferably Azure) for data storage and processing.
Data Architecture: Expertise in data architecture to ensure best practices in scaling, cost efficiency, and performance optimization.
If youre passionate about building scalable data solutions and thrive in a fast-paced environment, wed love to hear from you!

Requirements:
Minimum 5 years of hands-on experience designing, building, and maintaining large-scale data pipelines for both batch processing and streaming use cases.
Deep expertise in Python and SQL, with a focus on writing clean, performant, and maintainable code.
Strong analytical and problem-solving skills, with the ability to break down complex technical challenges and align solutions to business objectives.
Solid background in data modeling, analytics, and designing architectures for scalability, performance, and cost efficiency.
Practical experience working with modern OLAP systems and cloud data platforms, including Databricks, Snowflake, or BigQuery.
Familiarity with AI agent protocols (such as A2A, MCP) and LLM-related technologies (e.g., vector databases, embeddings) is a plus.
AI-savvy, with comfort adopting AI tools and staying current with emerging AI trends and technologies.

Requirements:
Were looking for an experienced and passionate Staff Data Engineer to join our Data Platform group in TLV as a Tech Lead. As the Groups Tech Lead, youll shape and implement the technical vision and architecture while staying hands-on across three specialized teams: Data Engineering Infra, Machine Learning Platform, and Data Warehouse Engineering, forming the backbone of data ecosystem.
The groups mission is to build a state-of-the-art Data Platform that drives toward becoming the most precise and efficient insurance company on the planet. By embracing Data Mesh principles, we create tools that empower teams to own their data while leveraging a robust, self-serve data infrastructure. This approach enables Data Scientists, Analysts, Backend Engineers, and other stakeholders to seamlessly access, analyze, and innovate with reliable, well-modeled, and queryable data, at scale.
In this role youll
Technically lead the group by shaping the architecture, guiding design decisions, and ensuring the technical excellence of the Data Platforms three teams
Design and implement data solutions that address both applicative needs and data analysis requirements, creating scalable and efficient access to actionable insights
Drive initiatives in Data Engineering Infra, including building robust ingestion layers, managing streaming ETLs, and guaranteeing data quality, compliance, and platform performance

Develop and maintain the Data Warehouse, integrating data from various sources for optimized querying, analysis, and persistence, supporting informed decision-makingLeverage data modeling and transformations to structure, cleanse, and integrate data, enabling efficient retrieval and strategic insights
Build and enhance the Machine Learning Platform, delivering infrastructure and tools that streamline the work of Data Scientists, enabling them to focus on developing models while benefiting from automation for production deployment, maintenance, and improvements. Support cutting-edge use cases like feature stores, real-time models, point-in-time (PIT) data retrieval, and telematics-based solutions
Collaborate closely with other Staff Engineers across to align on cross-organizational initiatives and technical strategies
Work seamlessly with Data Engineers, Data Scientists, Analysts, Backend Engineers, and Product Managers to deliver impactful solutions
Share knowledge, mentor team members, and champion engineering standards and technical excellence acros

Requirements:
8+ years of experience in data engineering, software engineering, or MLOps, with hands-on leadership in designing modern data platforms and distributed systems.
Proven experience implementing GenAI applications or infrastructure (e.g., building RAG pipelines, vector search, or custom LLM integrations).
Deep understanding of big data technologies (Kafka, Spark, Iceberg, Presto, Airflow) and cloud-native data stacks (e.g., AWS, GCP, or Azure).
Proficiency in Python and experience with GenAI frameworks like LangChain, LlamaIndex, or similar.
Familiarity with modern ML toolchains and model lifecycle management (e.g., MLflow, SageMaker, Vertex AI).
Experience deploying scalable and secure AI solutions with proper attention to privacy, hallucination risk, cost management, and model drift.
Ability to operate in ambiguity, lead complex projects across functions, and translate abstract goals into deliverable solutions.
Excellent communication and collaboration skills, with a passion for pushing boundaries in both data and AI domains.

Requirements:
4+ yeasrs of experience as a Data Engineer
Expertise in designing and developing scalable data pipelines, ETL processes, and data architectures.
Proficiency in Python and SQL, with hands-on experience in big data technologies like Apache Spark and Hadoop.
Advanced knowledge of cloud platforms (AWS, Azure, or GCP) and their associated data services.
Experience working with Imply and Apache Druid for real-time analytics and query optimization.
Strong analytical skills and ability to quickly learn and adapt to new technologies and tools.
You might also have
Hands-on experience with stream-processing frameworks like Apache Flink and Kafka for real-time data integration and analytics.
Knowledge of functional programming concepts, particularly using Scala.
Familiarity with data visualization tools like Tableau or Power BI for creating impactful dashboards.
Experience with machine learning frameworks or building ML pipelines and MLOps workflows.
Previous exposure to ad-tech data solutions or working within ad-serving ecosystems.

Requirements:
Bachelor's degree in Computer Science or equivalent practical experience.
At least 3 years of experience in data engineering or related roles.
Experience with big data Machine Learning - a must !
Proficiency in Python- must. Scala is a plus.
Experience with Big Data technologies including Spark, EMR and Airflow.
Experience with containerization/orchestration platforms such as Docker and Kubernetes.
Familiarity with distributed computing on the cloud (such as AWS or GCP).
Strong problem-solving skills and ability to learn new technologies quickly.
Being goal-driven and efficient.
Excellent communication skills and ability to work independently and in a team.

Requirements:
4+ years of hands-on experience in backend or data engineering, with a proven track record of building production-grade systems
Expertise in Python (or Java/Scala) with a deep understanding of data structures, algorithms, and performance trade-offs
Demonstrated experience designing and optimizing large-scale distributed data pipelines using technologies like Apache Spark, EMR, Databricks, Airflow, or Kubernetes
Strong command of a variety of storage engines, including Relational (PostgreSQL, MySQL), Document-based (MongoDB), Time-series / Search (ClickHouse, Elasticsearch), Key-value (Redis)
Familiarity with workflow orchestration tools such as Airflow, Dagster, or Prefect
Hands-on experience with message brokers like Kafka or RabbitMQ, and building event-driven systems
Solid foundation in software engineering best practices, including: CI/CD processes, Automated testing, Monitoring, Scalable system design
Experience in building and launching end-to-end data products that are core to business operations
Comfortable experimenting with AI tools and large language models (LLMs) for automation and data enrichment

Requirements:
3+ years of hands-on experience in data engineering, including building and optimizing scalable data pipelines
5+ years of experience as a software developer, preferably in Python
Strong algorithmic background, including: Development and optimization of machine learning models, Implementation of advanced data algorithms
Experience working with cloud ecosystems, preferably AWS (S3, Glue, EMR, Redshift, Athena) or equivalent platforms (Azure, GCP)
Expertise in extracting, ingesting, and transforming large-scale datasets in an efficient and reliable manner
Deep knowledge of big data platforms such as: Apache Spark, Databricks, Elasticsearch, and Kafka particularly for real-time data streaming and processing
(Nice-to-have) Hands-on experience working with Vector Databases and embedding techniques, with a focus on search, recommendations, and personalization.
AI-savvy: comfortable working with AI tools and staying ahead of emerging trends.