Senior Data Architect / Lead Data Engineer

Role OverviewWe are seeking a Senior Data Architect / Lead Data Engineer with strong hands-on data engineering experience and telecom domain expertise. The ideal candidate can design and build modern data platforms, data pipelines, governed data products, semantic layers, metadata models, ontologies, knowledge graphs, and AI-ready data foundations. This role requires experience working with telecom and telephone service provider data, including carrier invoices, usage records, service inventories, phone number inventories, device inventories, rate plans, contracts, service orders, billing adjustments, and provider reconciliation. The candidate should understand DLM / Telecom Lifecycle Management, TAM / Telephone Asset Management, Telecom Expense Management, phone number lifecycle, device management, device tracking, worker movement, cost allocation, service activation, suspension, disconnect, reassignment, and retirement. Key ResponsibilitiesDesign and implement enterprise data architectures for telecom lifecycle management, telephone asset management, telecom expense management, device tracking, provider data integration, and AI-ready data foundations. Build data pipelines across APIs, databases, files, SaaS platforms, SFTP feeds, carrier portals, billing feeds, usage extracts, inventory exports, HR systems, finance systems, procurement systems, ITSM systems, and asset management platforms. Ingest, validate, normalize, reconcile, and govern data provided by telephone service providers, including invoices, usage records, phone numbers, devices, SIM/eSIM records, plans, contracts, service orders, disconnects, and billing adjustments. Design data models for employees, phone numbers, devices, SIM/eSIM, carriers, plans, contracts, invoices, usage, cost centers, departments, locations, service status, device status, ownership, assignment history, and lifecycle events. Support end-to-end phone number lifecycle management, including allocation, activation, assignment, transfer, reassignment, suspension, disconnect, retirement, and reconciliation with carrier/provider records. Support end-to-end device management and tracking, including procurement, inventory, assignment, activation, user mapping, location tracking, movement, replacement, loss/damage, return, deactivation, disposal, and audit history. Support telecom expense management use cases, including invoice validation, duplicate charge detection, inactive line detection, unused device detection, orphaned number detection, rate-plan comparison, cost allocation, chargeback/showback, anomaly detection, and savings identification. Build governed data products, metadata models, semantic definitions, ontologies, knowledge graphs, GraphRAG patterns, and vector/search indexes to support dashboards, analytics, AI agents, and intelligent automation. Establish data quality, lineage, governance, observability, security, access control, reconciliation, and compliance controls across telecom and enterprise data assets. Design restartable, auditable, and traceable pipelines using batch IDs, run IDs, error handling, retry logic, duplicate detection, reconciliation, exception reporting, and operational monitoring. Required Experience and Skills10-15+ years of experience in data architecture, data engineering, enterprise data platforms, telecom data solutions, or related technology domains. Strong hands-on experience with SQL, Python, ETL/ELT, APIs, CDC, batch ingestion, file ingestion, orchestration, data modeling, and production-grade pipeline delivery. Experience with modern data platforms such as Snowflake, Databricks, AWS, Azure, GCP, data warehouses, lakehouses, data mesh, and data fabric. Experience with metadata, lineage, data quality, governance, access control, data products, data contracts, and semantic modeling. Experience integrating telecom provider data through APIs, carrier portals, SFTP/file feeds, CSV/Excel extracts, billing reports, inventory exports, usage extracts, and telecom expense platforms. Experience reconciling internal enterprise records with carrier/provider records to identify billing mismatches, inactive services, duplicate charges, unassigned devices, incorrect cost centers, and lifecycle gaps. Ability to model telecom entities and relationships across employees, phone numbers, devices, SIM/eSIM, carriers, plans, contracts, invoices, usage, service orders, cost centers, departments, locations, and lifecycle status. Strong communication skills with the ability to work with telecom operations, finance, procurement, HR, ITSM, security, data, AI, and client stakeholders. AI, Semantic, and Context Engineering ExperienceExperience or strong interest in LLMs, GenAI, intelligent agents, RAG, GraphRAG, knowledge graphs, vector databases, ontologies, semantic modeling, metadata-driven automation, AI evaluation, and enterprise AI integrations. Ability to design AI-ready context layers that connect telecom assets, phone numbers, devices, ownership, provider records, billing data, usage data, lifecycle events, business meaning, lineage, and governance. Ability to support AI agents that can answer questions about telecom inventory, device assignment, phone number ownership, billing anomalies, usage trends, lifecycle status, provider discrepancies, service risk, and cost optimization. Experience with AI-assisted engineering tools such as Claude Code, Cursor, GitHub Copilot, Windsurf, OpenAI, Gemini, agentic frameworks, and Model Context Protocol is preferred. Leadership and MindsetStartup mindset with strong ownership, accountability, and independent execution. Hands-on across architecture, engineering, and delivery. Customer-first mindset with the ability to challenge assumptions and recommend practical solutions. Ability to mentor engineers, create reusable patterns, and help build Context66’s technical standards and delivery accelerators. EducationBachelor’s or Master’s degree in computer science, engineering, information systems, data, telecommunications, or a related field. Relevant cloud, data, AI, telecom, security, or architecture certifications are a plus. Why Join Context66Build a modern Data, AI, and Enterprise Architecture company. Solve high-value telecom, data, and AI problems. Work on telecom lifecycle, telecom expense, device management, provider data integration, and AI-ready context modernization. Influence technical strategy and grow into leadership. We are looking for builders who combine architecture depth, engineering discipline, telecom lifecycle understanding, and a strong commitment to customer outcomes.