Role Description 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. Main Responsibilities - Strategic Analytics Leadership: 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. - Data Architecture & Governance: 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. - Product Performance Measurement: Develop frameworks to assess product delivery velocity, backlog health, and customer impact using tools like Power BI, Salesforce, and internal product layer data. - Stakeholder Engagement: 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. - Advanced Modeling & Forecasting: 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. - Mentorship & Enablement: 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. - Automation & Workflow Engineering: 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. - Code & No-Code Environment Fluency: 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. Qualifications - 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. Preferred Skills - 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. Compensation This information reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual pay is based on skills, experience and other relevant factors. - $132,232 - $176,310 in these states: AL, AR, AZ, FL, GA, IA, ID, IN, KS, KY, LA, ME, MO, MS, MT, ND, NE, NM, OH, OK, PA, SC, SD, TN, UT, VT, WI, WV, WY - $138,844 - $185,124 in these states: CO, HI, MI, MN, NC, NH, NV, OR, RI - $145,456 - $193,940 in these states: AK, CA, CT, DC, DE, IL, MA, MD, NJ, NY, TX, VA, WA Benefits - Lumen offers a comprehensive package featuring a broad range of Health, Life, Voluntary Lifestyle benefits and other perks that enhance your physical, mental, emotional and financial wellbeing.

Role Description We are looking for a Software Engineer who is eager to learn and grow while helping build and support Linux- and cloud-based data systems. In this role, you’ll work closely with experienced engineers to contribute to AWS-based data ingestion, ETL, and storage solutions that enable analytics, simulation, and ML training across the company. - Create robust and resilient pipelines to process massive daily volumes of data created by vehicle fleets and simulation environments. - Build and support scalable pipelines as part of Torc’s Data Factory to deliver data for ML training at scale. - Scale Torc’s data lake through a distributed storage system, data crawling and discovery. - Promote and protect the integrity of data through validation, versioning, data provenance, and governance. - Support the expansion of Torc’s data lake through acquisition of additional data sets from internal and external sources. - Assist in the development and delivery of cloud-based solutions. - Collaborate with teams specializing in perception, planning, control, mapping and vehicle testing to develop solutions that support product delivery. - Support the implementation of emerging cloud-based capabilities that can extend our technology stack and improve our ability to build, deploy and test safety-critical software for self-driving vehicles. - Participate in the team’s on-call rotation to support our deployed systems during business hours. Here’s a list of some of the technologies we use to make all the above happen: - Managed services powered by AWS (Lambda, SFN, Batch, EventBridge, Athena, Glue) - Linux / Bash - Docker - Terraform - Python - React/Javascript - On-Call Tooling (Datadog, AWS Cloudwatch) - Databricks Qualifications - BS/MS Degree in Computer Engineering, Computer Science, or related field. - Experience writing code using Python. - Practical experience with Docker and containerization. - A strong commitment to test-driven development patterns, continuous integration and delivery, and infrastructure as code. - Experience with Linux and general bash scripting. - Experience deploying, troubleshooting, monitoring and maintaining Linux systems. Requirements - Strong organizational, time management, and communication skills working with a team orientation and collaborative style. - Experience developing cloud-based serverless solutions. - Experience with pandas, numpy and other Python-based data analysis libraries and tooling. - Knowledge of AWS serverless architectures (Lambda, Batch, ECS Fargate, Glue, Athena). - Experience with data storage and acquisition patterns for robotics and advanced driver assistance systems. - Knowledge of different database architectures, including but not limited to relational and NoSQL databases, vector stores, data warehousing and clustered, distributed data stores. - Experience with the Databricks platform, particularly for serving data, visualizations and jobs. - Experience with scaling data for ML and AI workloads using Ray. Benefits - A competitive compensation package that includes a bonus component and stock options. - 100% paid medical, dental, and vision premiums for full-time employees. - 401K plan with a 6% employer match. - Flexibility in schedule and generous paid vacation (available immediately after start date). - AD+D and Life Insurance.

• Design, build, and maintain scalable data pipelines using Python, Spark, and Airflow to support our core data acquisition and entity resolution engines. • Collaborate cross-functionally with AI/ML and Product teams to implement new features and AI-native products. • Proactively identify and resolve bottlenecks in our complex ETL processes, bringing a fresh perspective to refine and optimize our existing codebase. • Contribute to a robust engineering culture through rigorous code reviews, unit testing, and clear communication of design decisions. • Own the end-to-end delivery of roadmap tasks within two-week sprints, ensuring work meets high standards for quality, documentation, and performance. • Participate in roadmap planning and story refinement, eventually taking ownership of major epics that drive our long-term product defensibility.

Role Description Own the design, development, and maintenance of data models within Snowflake that serve as the foundation for business metrics, reporting, and analytics across the organization. - Partner closely with business stakeholders to translate operational definitions and KPIs into reliable, well-documented data structures. - Build and maintain dbt models that power our core metrics layer, ensuring accuracy, consistency, and reusability across teams. - Manage and evolve our Snowflake data warehouse, including schema design, performance optimization, and data organization best practices. - Collaborate with analytics and BI teams to ensure the data layer supports self-serve reporting and business review cadences. - Contribute to data modeling standards and best practices that allow the team to scale with confidence. - Participate in strategic discussions around data architecture and help evaluate trade-offs across build vs. buy decisions. Qualifications - 5+ years of experience in a data engineering or analytics engineering role. - Strong proficiency in SQL and hands-on experience with Snowflake, including warehouse management, query optimization, and data modeling patterns. - Experience with dbt (Core or Cloud) for building and maintaining modular, tested transformation pipelines. - Demonstrated ability to work closely with business stakeholders to define and implement metrics and data definitions — not just build pipelines. - Solid understanding of dimensional modeling, data vault, or other warehouse modeling approaches. - Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect. - Hands-on experience with AWS data services - S3, MWAA, and CloudWatch - supporting production data pipelines in a cloud-native environment. - Ability to work through ambiguity and exercise good judgment when translating loosely defined business requirements into structured data models. - Bachelor's degree in Computer Science, Engineering, Math, or a related field, or equivalent practical experience. Requirements - The base salary range for this role varies by location and is aligned to market benchmarks. - Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range. - Final compensation is determined based on location, experience, skills, and internal equity. Benefits - This role is eligible for a 10% target annual bonus. - Base Salary: $160,000 - $174,000. - Total Target Compensation (TTC): $176,000 - $191,400. - *Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.). Additionally, stock options, paid benefits, and employee perks are part of your total rewards. Company Description We’re Cleerly – a healthcare company that’s revolutionizing how heart disease is diagnosed, treated, and tracked. - Founded in 2017 by one of the world’s leading cardiologists. - Raised $223M in Series C funding in 2022. - Received an additional $106M in Series C extension funding in December 2024. - Most teams work remotely with access to offices in Denver, New York, Dallas, and Lisbon. - Committed to providing safe and effective medical software that meets customer needs.