Senior Lead Data Architect

Lumen Technologies•,

14h•$132,232 - $193,940•Remote

About The Position

Lumen is seeking a strategic and technically adept Senior Lead Data Architect to lead high-impact analytics initiatives across the Product organization. This role goes beyond traditional business intelligence — the ideal candidate will bring deep strengths in automation, advanced analytics, and scalable reporting, with working knowledge of data science methods and the versatility to operate across both code and no-code environments. This individual will drive data discipline, enable predictable delivery, and support innovation by transforming complex datasets into actionable insights. They will serve as a thought partner to product leadership, influencing decisions through rigorous analysis, automated workflows, and enterprise-grade reporting frameworks. You’ll be at the heart of Lumen’s transformation, enabling data-driven decision-making across product innovation, delivery, and customer experience. This role offers visibility to executive leadership and the opportunity to shape how data informs our future.

Requirements

Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.
8+ years of experience in data analytics, with at least 3 years in a senior or principal role.
Proficiency in SQL, Python, and enterprise data systems (e.g., CDW, SAP ECC); hands-on experience building automated workflows, scheduled pipelines, and data transformation scripts.
Strong understanding of telecom product structures, service definitions, and port-based connectivity models.
Experience with audit preparation and investor-facing reporting frameworks.

Nice To Haves

Familiarity with Palantir Foundry and ontology-driven data modeling.
Experience in product operations, backlog management, and agile delivery metrics.
Ability to translate business strategy into measurable outcomes and scalable dashboards.
Data science exposure preferred — familiarity with machine learning concepts, statistical modeling, and libraries such as scikit-learn, statsmodels, or similar; ability to collaborate with or direct data science teammates.
Experience with no-code and low-code analytics tools (e.g., Power BI, Tableau, Alteryx, Dataiku, or similar), with the ability to serve both technical and non-technical users within the same workflow.
Experience designing and managing automated reporting systems, including scheduled delivery, exception alerting, and self-serve analytics portals.
Comfort working across the full analytics stack — from raw data extraction and transformation to polished executive-facing deliverables — without requiring handoffs between teams.

Responsibilities

Partner with Product Ops and Product Houses to define and measure innovation vs. predictability trade-offs, surfacing gaps in current metrics and proposing new KPIs aligned to business goals.
Lead efforts to unify data sources across legacy systems and modern platforms (e.g., CDW, Palantir Foundry), ensuring consistency, auditability, and scalability of analytics solutions.
Develop frameworks to assess product delivery velocity, backlog health, and customer impact using tools like Power BI, Salesforce, and internal product layer data.
Collaborate with Product Managers, Engineering, Finance, and Sales to align data definitions and reporting logic, ensuring transparency and trust in shared dashboards and executive summaries. Design and maintain standardized, automated reporting pipelines that reduce manual effort and deliver consistent, on-demand insights to stakeholders at all levels.
Build predictive models and apply data science techniques — including regression, clustering, and time-series analysis — to support funnel analysis, ARPU forecasting, churn prediction, and incremental sales tracking for high-bandwidth services (e.g., Ethernet, IPVPN, NAS). Translate model outputs into business-ready narratives for non-technical audiences.
Guide junior team members and cross-functional teams in best practices for data handling, visualization, and storytelling. Champion upskilling and bi-directional data literacy across the Product organization.
Design and implement automated data pipelines, scheduled reports, and alert-driven workflows that reduce manual processing and increase the speed and reliability of analytics delivery. Leverage scripting (Python, SQL) alongside automation platforms to operationalize recurring analytics at scale.
Operate effectively in both programmatic (Python, SQL, Jupyter) and no-code/low-code environments (Power BI, Tableau, Alteryx, or similar), selecting the right tool for each audience and use case. Empower business users through self-serve analytics while maintaining rigor in code-based workflows for complex analysis.