דרושים » דאטה » Database Administrator (DBA)

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:
10+ years of experience in data-related roles, with at least 5 years in a leadership position (ideally within a tech or AI-driven startup environment).
M.Sc. or PhD in Data Science/Computer Science/Engineering/Statistics, or a related field.
Extensive experience with cloud platforms (AWS, GCP, or Azure) and modern data warehouses (Snowflake, BigQuery, or Redshift).
Proficiency in data technologies, such as SQL, Python, R, Looker and big data tools (e.g., Hadoop, Spark).
Proven experience in leveraging data for product development, business intelligence, and operational optimization.
Strong track record of building and managing cross-functional data teams and influencing across all levels of an organization.
Excellent communication skills, with the ability to convey complex data insights in an accessible manner to non-technical stakeholders.

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:
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:
5+ years of experience leading, designing, and implementing data architecture at an enterprise level.
5+ years of experience working with relational databases (for example, PostgreSQL, MSSQL or MySQL).
2+ years of experience working with Elasticsearch.
2+ years of experience working with non-relational databases (for example, MongoDB or Cassandra).
Be exceptionally strong in SQL development - complex queries and performance tuning.
Implement and manage ETL/ELT processes for data integration and transformation.
Proficiency in Azure and Azure Data Factory for data integration and transformation.
Experience with Databricks or similar data processing platforms.
Experience with Scrum and Agile methodology.
Fluent in spoken and written English.
Advantages
Experience with big data technologies such as Hadoop, Spark, or Kafka.
Familiarity with data warehousing solutions.
Knowledge of data governance and data security practices.
Certification in Azure or other relevant technologies.
University or college degree in Computer Science or a related discipline.

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.

Requirements:
Requirements:
3+ years of experience in data engineering or a related field.​
Proficiency in SQL and experience with modern lakehouse modeling
Hands-on experience with data pipeline orchestration tools like Apache Airflow.​
Experience with DBT for data transformation and modeling.​
Familiarity with data visualization tools such as Tableau.​
Strong programming skills in languages such as Python or Java.​
Hands-on experience with AWS data solutions (or other major cloud vendor)
Excellent problem-solving skills and attention to detail.​
Strong communication skills and the ability to work collaboratively in a team environment.​
Relevant academic degree in Computer Science, Engineering, or related field (or equivalent work experience).

Preferred Qualifications:
Experience in the travel or insurance industries.​
Familiarity with Mixpanel or similar analytics platforms.​
Knowledge of data security and privacy best practices.

Requirements:
Have 5+ years of hands-on experience in designing and maintaining big data pipelines across on-premises or hybrid cloud environments, with proficiency in both SQL and NoSQL databases within a SaaS framework.
Proficient in one or more programming languages: Python, Scala, Java, or Go.
Experienced with software engineering best practices and automation, including testing, code reviews, design documentation, and CI/CD.
Experienced in building and designing ML/AI-driven production infrastructures and pipelines.
Experienced in developing data pipelines and maintaining data lakes on AWS - big advantage.
Familiar with technologies such as Kafka, Snowflake, MongoDB, Airflow, Docker, Kubernetes (K8S), and Terraform - advantage.
Bachelor's degree in Computer Science or equivalent experience.
Strong communication skills, fluent in English, both written and verbal.
A great team player with a can-do approach.

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:
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.