Make your mark in a role where strong analytics engineering directly supports critical operations at scale. You'll tackle complex data challenges and help shape modern data practice. Join a focused, supportive team that cares about both outcomes and sustainability by helping to define how performance, resilience, security, and cost are measured across a large organization **What you'll be doing:** - Build and maintain scalable data models and transformation pipelines for infrastructure, security, and operational analytics using SQL and modern data tooling - Treat the platform as code: version-controlled transformations, tested pipelines, peer-reviewed changes, and reproducible environments - Own the security data model, including entity resolution across asset and identity sources, a common cross-domain schema, and partitioning, retention, and cost strategy - Deliver curated datasets, semantic layers, and dashboards that make reporting on system performance, outages, security health, and cost consistent and trusted - Enforce data quality and governance through validation checks, freshness SLAs, reconciliation, lineage tracking, access controls, and sensitive-data handling - Partner with engineering, infrastructure, and security teams to define metrics, standardize definitions, and improve data accessibility - Analyze vendor performance and cost data to support infrastructure investment and optimization decisions - Improve the analytics platform itself: workflow orchestration, query performance, and architecture **Must haves:** - 3+ years in analytics engineering, data analytics, or a similar technical role - Bachelor's degree in Statistics, Computer Science, Engineering, Finance, or a related analytical field - Strong SQL, plus experience building data models (dimensional modeling, warehousing, or data lake environments) - Hands-on experience with a transformation framework such as dbt, and with version control and reproducible data workflows - Experience with BI tools (Power BI, Tableau) and semantic layer design - Working knowledge of data quality, governance, and privacy practices - Experience with technical data domains such as infrastructure, systems, security, or engineering telemetry - Ability to translate complex technical data into insights stakeholders can act on **Nice to haves:** - Python for data transformation, pipeline development, or analysis - Workflow orchestration tools (e.g., Airflow) and data lake table formats (Delta, Iceberg) - Understanding of cloud systems and observability metrics - Experience with vendor performance or financial analysis - AI/ML governance frameworks and associated data controls - End-to-end MLOps: feature engineering on large-scale telemetry (pandas/Polars, PySpark), anomaly detection and classification models (scikit-learn, XGBoost, PyOD), and experiment tracking with a versioned model registry (MLflow) **In this role you may be exposed to adult content**