לוח משרות דרושים מהנדס/ת דאטה/DATA ENGINEER עבודה היברידית

Requirements:
What You'll Need:

5+ years of experience in data engineering with 2+ years in a technical leadership or management role.

Strong technical background in Go and/or Java with hands-on experience in big data technologies.

Proven experience managing and scaling engineering teams.

Deep expertise with distributed databases (Cassandra, Elasticsearch) and production-grade data pipeline architecture.

Track record of delivering complex data infrastructure projects on time.

Excellent communication skills - ability to translate technical concepts to non-technical stakeholders.

Strong leadership abilities including mentoring, conflict resolution, and team building.

BS/MS in Computer Science or related field, or equivalent experience.

Experience with cloud platforms (AWS, GCP, Azure).

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Bonus Points:

Experience with identity and access management concepts or security analytics platforms.

Knowledge of security analytics and threat detection.

Contributions to open-source projects or technical community leadership.

Background in cybersecurity or security analytics.

Experience managing distributed or remote teams.

Prior experience at a high-growth technology company.

Requirements:
Must Haves:
5+ years in data engineering with strong Python & software engineering skills
Extensive experience with cloud platforms (AWS, GCP, Azure) and cloud-native architectures
Deep knowledge of relational (PostgreSQL) and NoSQL databases
Experience designing and implementing data warehouses and data lakes
Strong understanding of data modeling techniques
Expertise in data manipulation libraries (e.g., Pandas) and big data frameworks
Experience with data validation tools (Pydantic, Great Expectations)
Proficiency in writing and maintaining unit and integration tests (e.g., Pytest)
Nice to Haves / Advantage:
Apache Iceberg, Apache Spark, or modern data stack tools (DBT, DuckDB, Dagster, Airflow, Prefect)
Backend system design: RESTful APIs, microservices, event-driven systems, RabbitMQ, Kafka
Containerization & IaC: Docker, Kubernetes, Terraform
Stream processing: Kafka, Flink
Knowledge of compliance (GDPR, CCPA)
Mentoring or leading small teams
Excellent communication and ability to explain technical concepts clearly
Relevant cloud or data certifications.

Requirements:
Who You Are (Success Profile):
You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.
You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.
You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.
You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback-knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.
What Were Looking for (Minimum Qualifications):
7+ years of experience in data engineering with a proven track record of designing and building large-scale data systems
Demonstrated mastery of Apache Spark (PySpark preferred) and a deep architectural understanding of distributed data processing frameworks
Expert-level proficiency in SQL, data modeling, and the principles of modern data warehousing and data lake architecture
What Will Make You Stand Out (Preferred Qualifications):
Direct experience working in cybersecurity, threat intelligence, or a related security-focused domain
Proven experience building production-grade data solutions specifically using Databricks
A security-first mindset and a strong curiosity for understanding how systems can be monitored and secured.

Requirements:
5+ years in data engineering.
Expertise in Apache Spark and Spark Structured Streaming.
Hands-on experience with Apache Iceberg or similar data lakes.
Proficiency in Scala, Java, or Python.
Knowledge of big data technologies (Hadoop, Hive, Presto).
Experience with cloud platforms (AWS, Azure, GCP) and SQL.
Strong problem-solving, communication, and collaboration skills.

Requirements:
What you'll bring:

3+ years experience in data engineering or platform engineering roles.

Strong programming skills in Python and SQL.

Experience with orchestration platforms like Airflow/Dagster/Temporal.

Experience with MPPs like Snowflake/Redshift/Databricks.

Hands-on experience with cloud platforms (AWS) and their data services.

Understanding of data modeling, data warehousing, and data lake concepts.

Ability to optimize data infrastructure for performance and reliability.

Experience working with containerization (Docker) in Kubernetes environments.

Familiarity with CI/CD concepts.

Fluent in English, both written and verbal.

And it would be great if you have (optional):

Experience with big data processing frameworks (Apache Spark, Hadoop).

Experience with stream processing technologies (Flink, Kafka, Kinesis).

Knowledge of infrastructure as code (Terraform).

Experience building analytics platforms.

Experience building clickstream pipelines.

Familiarity with machine learning workflows and MLOps.

Experience working in a startup environment or fintech industry.

Requirements:
MUST-HAVE REQUIREMENTS
At least 4 years of strong SQL server experience (core requirement)
Experience working with legacy or imperfect schemas Python for data processing and automation
Practical experience with MongoDB (modeling, indexing, querying)
Excellent English communication skills
Comfortable working hands-on in a fast-moving startup environment
NICE TO HAVE (ADVANTAGES)
BI tools (Power BI, Looker, Tableau, etc.).

Requirements:
5+ years of hands-on experience in backend or data engineering, including 2+ years at a senior level delivering production systems
Strong coding skills in Python, Kotlin, Java or Scala with emphasis on clean, testable, production-ready code
Proven track record designing, building and operating distributed data pipelines and storage (batch or streaming)
Deep experience with relational databases (PostgreSQL preferred) and working knowledge of at least one NoSQL or columnar/analytical store (e.g. SingleStore, ClickHouse, Redshift, BigQuery)
Solid hands-on experience with event-streaming platforms such as Apache Kafka
Familiarity with data-orchestration frameworks such as Airflow
Comfortable with modern CI/CD, observability and infrastructure-as-code practices in a cloud environment (AWS, GCP or Azure)
Ability to break down complex problems, communicate trade-offs clearly, and collaborate effectively with engineers and product partners
Bonus Skills
Experience building data governance or security/compliance-aware data platforms
Familiarity with Kubernetes, Docker, and infrastructure-as-code tools
Experience with data quality frameworks, lineage, or metadata tooling.

Requirements:
5+ years experience working as a BI Developer or as a Data Engineer
Highly skilled with SQL and building ETL workflows - Mandatory
2+ years Experience in Python - Mandatory.
Experience developing in ETL tools like SSIS or Informatica - Mandatory
2+ years experience with Airflow & DBT - Mandatory
Experience developing data integration processes, DWH, and data models.
Experience with columnar DB and working with Pipelines and streaming data (SingleStore/Snowflake) - advantage.
Experience working with BI reporting tools (Power BI, Tableau, SSRS, or other)
Experience with cloud-based products (AWS, Azure) - advantage.
Enhance operational efficiency and product innovation using AI (co-pilot/cursor AI)
Preferred Qualifications:
Familiarity with CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes)
Experience with Git (or other source control)
Familiarity with AI Agents and Models to improve reliability and Data Integrity
Experience in Kafka is an advantage.

Requirements:
5+ years of professional experience in software engineering, data engineering, or product ownership roles.
Strong development experience with Python or Golang (both - advantage).
Demonstrated ability to translate business requirements into technical roadmaps and deliverables.
Experience working in Agile/Scrum environments and managing product backlogs.
Experience with orchestration and containerization (Kubernetes, Docker).
Familiarity with Linux and cloud infrastructure (especially AWS).
Preferred Qualifications:
Bachelors or masters degree in computer science, Engineering, or related field.
Familiarity with data warehousing and modern data stack technologies.
Proven hands-on experience with Apache Airflow, and Apache Spark.
Knowledge of message queues and real-time systems (RabbitMQ, Redis, Kinesis).
Knowledge of DevOps and CI/CD tools.
Strong stakeholder management and communication skills.
Ability to balance technical depth with strategic vision.

Requirements:
Preferred Qualifications:
Experience with security-focused data solutions.
Deep expertise with Splunk and AWS services (S3, SQS, SNS, Stream).
Advanced understanding of distributed systems.
Strong Linux systems knowledge.
Experience with real-time data processing architectures.

Who You Are:
4+ years of hands-on data engineering experience in cloud-based SaaS environments.
Deep expertise in PySpark, Python, and SQL optimization.
Advanced knowledge of AWS, Azure, and GCP cloud architectures.
Proven track record implementing production-scale data systems.
Extensive experience with distributed computing and big data processing.
Strong collaboration skills and technical communication abilities.

Requirements:
3+ years of experience in Data Engineering, including cloud-based data solutions.
Proven expertise in implementing large-scale data solutions.
Proficiency in Python, PySpark.
Experience with ETL / ELT processes.
Experience with cloud and technologies such as Databricks (Apache Spark).
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.