What You’ll Do:

📍Design and build complex data pipelines to ingest, process, and transform data from a variety of sources, especially logs and textual inputs. 📍Collaborate closely with Software Engineering and Product teams to ensure data is accessible and usable. 📍Develop efficient ETL processes using frameworks such as DBT, Airflow, or their equivalents. 📍Own and optimize your data environment (e.g., Snowflake), focusing on performance tuning, governance, and reliability. 📍Build dashboards for various company-wide use cases. 📍Implement best practices for data management, quality assurance, and security within cloud infrastructures (AWS). 📍Enable ML and analytics teams by building pipelines that feed feature stores and model training workflows.

Requirements

📍4+ years of hands-on Data Engineering experience, in a cybersecurity or security-adjacent environment. 📍Proficiency in Python and SQL, with proven experience handling large or unstructured data. 📍Familiarity with data warehouse technologies (Snowflake, BigQuery). 📍Experience with big data infrastructure (Snowflake/Databricks), orchestration tools (Airflow), and cloud platform (AWS). 📍Solid understanding of data governance, quality assurance, and pipeline observability. 📍Ability to deliver end-to-end solutions—from ingestion to production-ready datasets with minimal supervision.

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