דרושים » דאטה » Data Infra Engineer

Requirements:
Mandatory Qualifications:
3+ Years of Experience: Demonstrated experience as a DevOps professional, with a strong focus on big data environments, or Data Engineer with strong DevOps skills.
Data Componments Management: Experience with managing and design data infrasturacture such as Snowflake, PostgreSQL, Kafka, Aerospike, Object Store.
DevOps Expertise: Proven experience in creating, establishing, and managing big data tools, including automation tasks. Extensive knowledge of DevOps concepts and tools, including Docker, Kubernetes, Terraform, ArgoCD, Linux OS, Networking, Load Balancing, Nginx etc.
Programming Skills: Proficiency in programming languages such as Python and Object-Oriented Programming (OOP) languages with an emphasis on the big data processing (like PySpark). Experience with scripting languages like Bash and Shell for automation tasks.
Cloud Platforms: Hands-on experience with major cloud providers such as Azure, Google Cloud, or AWS.
Preferred Qualifications:
Performance Optimization: Experience in optimizing performance for big data tools and pipelines - Big Advantage.
Security Expertise: Experience in identifying and addressing security vulnerabilities within the data platform - Big Advantage.
CI/CD Pipelines: Experience in designing, implementing, and maintaining Continuous Integration/Continuous Deployment (CI/CD) pipelines Advantage.
Data Pipelines: Experience in building big data pipelines - Advantage.

Requirements:
5+ Years of Experience in data engineering and big data at large scales. - Must
Extensive experience with modern data stack - Must:
Snowflake, Delta Lake, Iceberg, BigQuery, Redshift
Kafka, RabbitMQ, or similar for real-time data processing.
Pyspark, Databricks
Strong software development background with Python/OOP and hands-on experience in building large-scale data pipelines. - Must
Hands-on experience with Docker and Kubernetes. - Must
Expertise in ETL development, data modeling, and data warehousing best practices.
Knowledge of monitoring & observability (Datadog, Prometheus, ELK, etc)
Experience with infrastructure as code, deployment automation, and CI/CD.
Practices using tools such as Helm, ArgoCD, Terraform, GitHub Actions, and Jenkins.
Our stack: Azure, GCP, Databricks, Snowflake, Airflow, Spark, Kafka, Kubernetes, Neo4J, AeroSpike, ELK, DataDog, Micro-Services, Python, SQL
Your stack: Proven strong back-end software engineering skills, ability to think for yourself and challenge common assumptions, commitment to high-quality execution, and embrace collaboration.

Requirements:
3+ years of DevOps experience in a cloud-based production environment.
Strong expertise in Docker, K8s, and containerized application management.
Experience with AWS, Azure, or similar cloud platforms.
Hands-on experience with Infrastructure as Code (IaC) tools like Terraform.
Proficiency in CI/CD automation, including Jenkins, GitHub Actions, or similar
Knowledge of monitoring and logging tools
Strong scripting skills in Bash and experience with programming languages like Python or Java.
Experience with GitOps methodologies.
Familiarity with Istio and service mesh architectures.
Bonus Points For:
Hands-on experience managing data lakes or data warehouses.
Prior experience in Enterprise environments.
Strong problem-solving abilities and a passion for learning new technologies.
Ability to thrive in a fast-paced, dynamic environment and tackle challenges head-on.

Requirements:
5+ years of experience in data engineering, with at least 2-3 years experience in a Tech Lead role.
At least 3 years of hands-on experience with AWS, including services like S3, Redshift, Glue, Athena, Lambda, and RDS.
Expertise in DWH & Data Lake architectures, including columnar databases, data partitioning, and lakehouse concepts.
Strong experience with cloud data solutions like Redshift, Snowflake, BigQuery, or Databricks.
Proficiency in ETL/ELT tools (e.g., dbt, Apache Airflow, Glue, Dataflow).
Deep knowledge of SQL & Python for data processing and transformation.
Experience working with BI and visualization tools such as Power BI, Tableau, Looker, or similar.
Experience with real-time data streaming (Kafka, Kinesis, Pub/Sub) and batch processing.
Understanding of data modeling (Star/Snowflake), data governance, and security best practices.
Experience with CI/CD, infrastructure-as-code (Terraform, CloudFormation), and DevOps for data.
The personal competencies you need to have:
Excellent communication skills and the ability to work as a team.
Strong sense of ownership, urgency, and drive.
Ability to take the initiative, come up with ideas and solutions, and run it with a "getting things done" attitude.
Ability to work independently and manage tight deadlines.

Requirements:
4+ years in Data/Backend engineering with experience in designing, developing and optimizing streaming data pipelines using Apache Spark, Kafka, or similar technologies.
Dealing with data on high volume, high availability production systems
Practical experience with Python in the domain of data pipelines.
Experience with cloud-based data lake architectures (AWS S3, Google Cloud Storage).
Exposure to DevOps practices, CI/CD pipelines, and infrastructure as code.
Excellent problem-solving skills and the ability to work in a collaborative environment.

Requirements:
* B.Sc. in Computer Science, data Engineering, or a related field.
* 3+ years of hands-on experience in large-scale data infrastructures.
* Strong Python programming, with expertise in PySpark and Pandas.
* Deep knowledge of SQL and NoSQL databases, including performance optimization.
* Experience with ElasticSearch and AWS cloud services.
* Solid understanding of DevOps practices, Big Data tools, Git, and Jenkins.
* Familiarity with microservices and event-driven design.
* Strong problem-solving skills and a proactive, independent mindset. Advantages
* Experience with LangChain, ClickHouse, DynamoDB, Redis, and Apache Kafka.
* Knowledge of Metabase for data visualization.
* Experience with RESTful APIs and Node.js. Talent We Are Looking For Independent & Self-Driven Comfortable building from the ground up. Growth-Oriented Eager to develop professionally and take on leadership roles. Innovative Passionate about solving complex data challenges. Collaborative Strong communicator who works well with cross-functional teams. Adaptable Thrives in a fast-paced, dynamic environment with a can-do attitude. About the Company: Appdome's mission is to protect every mobile app worldwide and its users. We provide mobile brands with the only patented, centralized, data -driven Mobile Cyber Defense Automation platform. Our platform delivers rapid no-code, no-SDK mobile app security, anti-fraud, anti-malware, anti-cheat, anti-bot implementations, configuration as code ease, Threat-Events threat-aware UI / UX control, ThreatScope Mobile XDR, and Certified Secure DevSecOps Certification in one integrated system. With Appdome, mobile Developers, cyber and fraud teams can accelerate delivery, guarantee compliance, and leverage automation to build, TEST, release, and monitor the full range of cyber, anti-fraud, and other defenses needed in mobile apps from within mobile DevOps and CI/CD pipelines. Leading financial, healthcare, m-commerce, consumer, and B2B brands use Appdome to upgrade mobile DevSecOps and protect Android & IOS apps, mobile customers, and businesses globally. Today, Appdome's customers use our platform to secure over 50,000+ mobile apps, with protection for over 1 billion mobile end users projected.
Appdome is an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics.

Requirements:
7+ years of experience in DevOps or related fields, with experience in a leadership role.
Proven experience with container orchestration technologies like Docker and Kubernetes or Swarm.
Extensive experience with cloud computing platforms like AWS, Azure, or Google Cloud (Google Cloud is an advantage).
Experience with configuration management tools like Terraform, Ansible, Puppet, or Chef.
Experience with package managers for Kubernetes tools like Helm, Kustomize, and Kompose (Helm an advantage).
Advanced knowledge of CI/CD tools like GitLab CI/CD, Circle CI, Jenkins, or Argo CD.
Experience with database administration and management.
Advanced Bash scripting skills.
Proficiency in at least one programming language, such as Python or Go (a must).
Experience with monitoring and logging tools like Prometheus, Grafana, or Datadog.
Experience with microservices architecture, API gateways, or Reverse Proxy such as NGINX (an advantage).
Excellent communication and interpersonal skills, with the ability to influence and drive cross-functional initiatives.
Demonstrated leadership experience, including mentoring and guiding team members.

Requirements:
Minimum of 5 years of experience in the DevOps, with a proven track record of designing and implementing scalable and reliable infrastructure.
In-depth knowledge of cloud computing platforms such as AWS, Azure, or GCP, including services like EC2, S3, RDS, VPC, etc.
Strong experience with containerization and orchestration technologies such as Docker, Kubernetes, or ECS. Proficiency in scripting and programming languages such as Python, Shell, or Go.
Experience with CI/CD tools such as Jenkins, GitLab CI/CD, or CircleCI.
Solid understanding of networking concepts, security best practices, and infrastructure-as-code principles.
Excellent problem-solving skills and ability to troubleshoot complex issues across multiple systems. Strong communication and collaboration skills, with the ability to work effectively in a fast-paced, dynamic environment.
Outstanding communication skills (both oral and written) in Hebrew and English.

Requirements:
5+ years of experience as a Data Engineer or in a similar role.
Expertise in SQL and proficiency in Python for data engineering tasks.
Proven experience designing and implementing analytics-focused data models and warehouses.
Hands-on experience with data pipelines and ETL/ELT frameworks (e.g Airflow, Luigi, AWS Glue, DBT).
Strong experience with cloud data services (e.g., AWS, GCP, Azure).
A deep passion for data and a strong analytical mindset with attention to detail.
Bonus points:
Strong understanding of business metrics and how to translate data into actionable insights
Experience with data visualization tools (e.g., Tableau, Power BI, Looker)
Familiarity with data governance and data quality best practices
Excellent communication skills to work with cross-functional teams including data analysts, data scientists, and product managers.