Role Overview As an Analytics Engineer for Network Intelligence on Data & AI team, you will bridge the gap between Xplore's raw network and operational data and the dashboards, KPIs, and self-serve analytics that engineering, operations, and leadership teams rely on daily. You will own the semantic layer, curated Gold-tier analytical datasets across both on-prem and cloud data platforms, and Databricks AI/BI dashboards that surface real-time and historical network intelligence across Fiber, Fixed Wireless, and Satellite domains. This role is not limited to cloud migration work: it also requires designing, building, and operating analytical datasets, transformations, and reporting assets directly on-prem where business and operational needs require it. Unlike a pure data engineer who focuses on pipeline infrastructure, or a data scientist focused on modeling, your specialty is making data reliable, understood, and usable by the widest possible audience: you own metric definitions, dashboard governance, and the translation of operational data needs into clean, documented analytical assets. Key responsibilities include: • Define, document, and maintain canonical KPI and metric definitions for network health, incident volume, capacity utilization, and customer experience indicators in Unity Catalog. • Build and own the semantic layer (Databricks AI/BI datasets) underpinning self-serve analytics for network operations, engineering, and leadership stakeholders. • Design and build operational and executive dashboards in Databricks AI/BI covering real-time network health, incident trends, performance benchmarks, and customer impact metrics. • Develop automated alerting and correlation views that reduce noise for network operations teams and accelerate root cause identification. • Build curated Gold-tier analytical tables and transformation layers across both on-prem and cloud platforms using SQL and PySpark, applying business logic, aggregations, and dimensional modeling patterns. • Implement data quality checks and automated validation to ensure analytical outputs are accurate before reaching business consumers. • Partner with Data Engineers to surface upstream data quality issues and define requirements for Silver-to-Gold transformations. • Act as the subject matter expert for network and operations data consumers, supporting self-serve analytics adoption across engineering and ops teams. • Produce clear data dictionaries, lineage documentation, and onboarding guides for all owned analytical assets. • Support executive-level reporting with accurate, clearly structured KPI outputs tied to network quality and customer experience. The ideal candidate will possess: • 5+ years of analytics engineering, BI development, or data analysis experience in a data-intensive environment. • Expert SQL skills: complex analytical queries, window functions, dimensional modeling, and metric grain definitions at scale. • Hands-on Databricks experience: SQL Warehouses, AI/BI Dashboards, and Unity Catalog; or equivalent modern data stack experience (Snowflake, BigQuery+ Looker or dbt). • Strong understanding of data modeling concepts: star schema, slowly changing dimensions, and single-source-of-truth metric governance. • Demonstrated ability to translate ambiguous business questions into well-defined analytical datasets and dashboard specifications. • Proficiency in Python for lightweight transformations and pipeline scripting. • Excellent written and verbal communication skills; comfort presenting analytical findings to Director and VP-level audiences. Preferred Qualifications: • Experience building and supporting analytics or transformation layers on-prem as well as in Databricks/cloud environments; not limited to migration initiatives. • Background in network operations, telecommunications, or infrastructure analytics. • Familiarity with Grafana, Datadog, or Prometheus as complementary network observability tools. • Experience designing role-based access controls and data governance frameworks in a cloud or on-prem data platform. • Bachelor's degree in Computer Science, Information Systems, Mathematics, or a related field.