• Help build, maintain, and scale our data pipelines that bring together data from various internal and external systems into our data warehouse. • Partner with internal stakeholders to understand analysis needs and consumption patterns. • Partner with upstream engineering teams to enhance data logging patterns and best practices. • Participate in architectural decisions and help us plan for the company’s data needs as we scale. • Adopt and evangelize data engineering best practices for data processing, modeling, and lake/warehouse development. • Advise engineers and other cross-functional partners on how to most efficiently use our data tools.

• Orchestrate High-Velocity Workflows: Leverage advanced agentic coding tools (e.g., Cursor, multi-agent environments) to dramatically accelerate feature prototyping, code generation, and test coverage. • Own the Guardrails & Quality: Act as the ultimate reviewer and architect; define the specifications, establish repo-context guardrails, and review AI-accelerated output for hidden security risks, scale bottlenecks, and architectural alignment. • Build Scalable Application and Data Layers: Design, build, and maintain our data pipelines and application to service our hundreds of users. • Bridging the Data: Partner closely with product, client services, and data science teams to transform raw pipeline outputs into meaning. • Drive Greenfield Engineering: Take complete ownership of designing, building, and launching enterprise-grade applications, moving fluidly between rapid prototyping and bulletproof production systems.

Role Description As a Senior Data Engineer on the Network Intelligence team, you will own the end-to-end data pipeline function that powers Xplore's network analytics, observability, and AI capabilities across Fiber, Fixed Wireless, and Satellite platforms in both cloud and on-prem environments. You will design and operate the Bronze-to-Silver-to-Gold medallion pipelines for all network telemetry domains, ensuring that on-premise network modeling outputs are reliably structured, governed, and delivered as trusted enterprise Gold-tier assets. This role spans both cloud and on-premises platforms, including building, maintaining, and optimizing data pipelines and supporting infrastructure on-prem—not just migrating workloads to the cloud. Your work directly underpins predictive analytics, ML model training, and operational dashboards consumed by network engineering, operations, and executive audiences. You will work in a cross-functional squad alongside the Network Intelligence & Automation Specialist, Data Scientists, and value-stream AI/Observability engineers, contributing to platform standards and mentoring intermediate engineers on the team. - Design and build production-grade ELT pipelines in Databricks (PySpark, Delta Live Tables, Databricks Workflows) ingesting telemetry from RAN, transport, core, and cloud network platforms. - Own and maintain Bronze, Silver, and Gold tier datasets for all network domains, including schema governance, SLA adherence, and data quality standards in Unity Catalog. - Design, build, and maintain data pipelines and supporting components across both cloud and on-premises environments, including new on-prem solutions required for network telemetry and analytics workloads—not only cloud migration initiatives. - Deliver structured Bronze and Silver tier network modeling outputs into the Corporate Enterprise Gold tier, ensuring alignment with enterprise data architecture standards. - Apply partitioning, liquid clustering, and incremental load patterns to support large-scale time-series and event-driven network telemetry data. - Define and document canonical data models for network telemetry entities: cells, sites, circuits, alarms, and performance counters. - Instrument pipelines with data quality checks, alerting, and SLA monitoring to ensure operational reliability. - Partner with Data Scientists to produce feature-engineered Gold datasets suitable for ML model training and inference pipelines. - Collaborate with the GIS Data Engineer to integrate geospatial dimensions into network telemetry datasets. - Translate network engineering data requirements into structured pipeline specifications and delivery roadmaps. Qualifications - 7+ years of experience in data engineering with production-grade pipeline development. - Expert-level Databricks experience: Spark optimization, Delta Lake, Unity Catalog, and Databricks Workflows or Delta Live Tables. - Strong Python and PySpark for production data engineering. - Solid understanding of medallion architecture (Bronze / Silver / Gold) and enterprise data governance principles. - Experience with streaming or near-real-time ingestion patterns: Kafka, Event Hubs, or Databricks Auto Loader. - Proficiency with Azure Data Lake Storage Gen2 and Azure-based data platform components. - Solid data modeling skills for time-series and high-frequency event datasets. - Strong communication skills and demonstrated mentorship experience. Preferred Qualifications - Familiarity with network telemetry data sources: SNMP, syslog, NetFlow, RAN KPIs, or equivalent. - Telecom, FWA, or infrastructure analytics domain experience. - Experience with On-Prem Kafka is a bonus. - Familiarity with Terraform or infrastructure-as-code for pipeline environment provisioning. - Databricks Certified Data Engineer (Associate or Professional). - Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field. Condition of Employment As a condition of employment and in order to comply with industry related data security standards, this position is subject to the successful completion of a Criminal Background Check. Details will be supplied to applicants as they move through the selection process. Xplore is committed to creating an accessible environment and will accommodate disabilities during the selection process. Please let your recruiter know during the selection process of any accommodation needs.

• Define and evangelise target architectures that bridge our transactional, analytical, and AI systems, ensuring our data remains a competitive advantage. • Be a leader for our end-to-end data lifecycle, from OLTP schema design and migrations to OLAP governance, platform and analytics. • Partner with Technical Leads to provide hands-on guidance supporting feature delivery, tech debt paydown, and architecture improvements. (Hands-on for this role means staying close to the details but not necessarily coding) • Collaborate with Product Directors to understand product vision and translate this into deliverables, aligning commercial and engineering goals. • Lead our shift to an AI-First way of working, architecting the data foundations to enable AI in our products and championing the latest approaches to help drive productivity gains • Architect the future infrastructure required for LLM orchestration, Retrieval-Augmented Generation (RAG), and real-time model inference at scale • Mentor and coach Technical Leads/Senior Engineers on architectural and engineering excellence, continually raising our bar and supporting career growth • Act as a role model for technical leadership — work with pace, be pragmatic, and stay laser focused on outcomes not output • Head up architectural reviews for your domain and ensure all designs meet Arbor’s quality, reliability, security and compliance standards. • Collaborate with Staff Engineers and Principal Engineers to shape Arbor’s broader technology strategy, working outside your domain as needed • Partner with Engineering Management to help grow teams, identify and resolve systemic bottlenecks, ensuring the success of our strategic goals