Data Infra Engineer

Axonius is seeking a Data Infra Engineer. You will be responsible for designing and building all data, ML pipelines, data tools, and cloud infrastructure required to transform massive, fragmented data into a format that supports processes and standards. Your work directly empowers business stakeholders to gain comprehensive visibility, automate key processes, and drive strategic impact across the company.

• Design and Build Data Infrastructure: Design, plan, and build all aspects of the platform's data, ML pipelines, and supporting infrastructure.

• Optimize Cloud Data Lake: Build and optimize an AWS-based Data Lake using cloud architecture best practices for partitioning, metadata management, and security to support enterprise-scale operations.

• Lead Project Delivery: Lead end-to-end data projects from initial infrastructure design through to production monitoring and optimization.

• Solve Integration Challenges: Implement optimal ETL/ELT patterns and query techniques to solve challenging data integration problems sourced from structured and unstructured data.

• Experience: 3+ years of hands-on experience designing and maintaining big data pipelines in on-premises or hybrid cloud SaaS environments.

• Programming & Databases: Proficiency in one or more programming languages (Python, Scala, Java, or Go) and expertise in both SQL and NoSQL databases.

• Engineering Practice: Proven experience with software engineering best practices, including testing, code reviews, design documentation, and CI/CD.

• AWS Experience: Experience developing data pipelines and maintaining data lakes, specifically on AWS.

• Streaming & Orchestration: Familiarity with Kafka and workflow orchestration tools like Airflow.

• Containerization & DevOps: Familiarity with Docker, Kubernetes (K8S), and Terraform.

• Modern Data Stack: Familiarity with the following tools is an advantage: Kafka, Databricks, Airflow, Snowflake, MongoDB, Open Table Format (Iceberg/ Delta)

• ML/AI Infrastructure: Experience building and designing ML/AI-driven production infrastructures and pipelines.

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