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Come join the company that is reinventing cloud security and empowering businesses to thrive in the cloud. As the fastest-growing startup ever, Wiz is on a mission to help organizations secure cloud environments that will accelerate their businesses. Trusted by security teams all over the world, we have a proven track record of success and a culture that values world-class talent. Our Wizards from over 20 countries work together to protect the infrastructure of our hundreds of customers, including over 50% of the Fortune 100, who trust us to scan and secure over 230 billion files daily. We’re the leading player in a massive and growing market, but it’s still early enough for you to make a significant impact. At Wiz, you’ll have the freedom to think creatively, dream big, and use your full range of skills to contribute to our record growth. Come join our team and help us create secure cloud environments that allow the best companies to move faster.
Job Responsibility:
Design, build, and maintain data pipelines, datasets and catalogs for fast-growing products and business groups
Develop self-service data analytics solutions and infrastructure
Support ad hoc needs and requests of internal stakeholders
Collaborate with analysts, engineers, and internal customers from Product, Finance, Revenue, and Marketing
Requirements:
Bachelor’s or Master’s degree in a relevant technical field, or equivalent hands-on experience in software development or DevOps
3+ years of experience working as a Data Engineer, including end-to-end designing, orchestrating, and building cloud-based data pipelines (e.g., Airflow, Prefect, Dagster)
3+ years of experience with dimensional data modeling and data warehouse implementation, specifically MPP databases like BigQuery, Snowflake, and Redshift
Strong knowledge of Python and Python-based data analysis tools such as Jupyter Notebooks and pandas
Strong SQL writing skills. Ability to write highly performant queries
Strong track record of executing projects independently in dynamic environments
Fast understanding of data and business needs and ability to translate them into data models
Team player with excellent communication skills
Containerization (Docker): Essential for reproducible environments
Knowledge of software engineering best practices: CI/CD concepts, code reviews, and unit testing
Nice to have:
Production-level experience with dbt, including project design, transformation, testing, and documentation
Infrastructure-as-Code (Terraform): Managing cloud resources (S3 buckets, IAM roles) via code
CI/CD pipelines (GitHub Actions/Jenkins): Automating the testing and deployment of data models