Senior Manager GTM Data Analytics & Engineering

Senior Manager GTM Data Analytics & Engineering

Own and scale TeamViewer’s GTM data foundation. Lead analytics engineering across sales, marketing, and finance - building durable models and a reliable data layer.

TeamViewer provides a leading Digital Workplace platform that connects people with technology—enabling, improving and automating digital processes to make work work better. Our software solutions harness the power of AI and shape the future of digitalization.

We believe that our diverse teams and strong company culture are key to the success of our products and technologies, that hundreds of millions of users around the world and around 645,000 customers across all industries rely on. With more than 1,900 employees worldwide, we celebrate the unique perspectives and talents that each individual brings to the table and foster a dynamic work environment where new ideas thrive. Are you ready to join our team and make an impact?

This role is responsible for designing, building, and owning TeamViewer’s Revenue data architecture and analytics engineering layer. The focus is on creating a scalable, reliable data foundation that supports GTM reporting, forecasting, and advanced analytics across Marketing, Sales, Customer Experience, and Finance.

Rather than primarily producing reports, this role owns how GTM data is ingested, transformed, modeled, and served. From the architecture level, you will define the data models, pipelines, and governance that power executive reporting and self‑service analytics. You will lead a small team of data analysts and work together with cross-functional data engineering resources, to operate hands‑on in architecture and implementation.

This role sits at the intersection of Data Engineering, Data Analytics, and RevOps, with a strong emphasis on building durable systems over one‑off reporting. The right candidate is a player/coach with the ability to design required changes and coach/train others to implement them.

Responsibilities

Data Architecture & Analytics Engineering

•

Design and own the end‑to‑end GTM data architecture, from source systems through transformation layers to BI consumption

•

Build and maintain scalable data models that support full‑funnel and revenue analytics

•

Develop and manage ELT/ETL pipelines integrating CRM, Marketing Automation, Finance, and Customer platforms

•

Ensure data pipelines are reliable, monitored, performant, and well‑documented

•

Partner with central data and engineering teams where applicable to align with broader architecture standards

Revenue & GTM Data Modeling

•

Own the canonical data models for Marketing, Sales, Pipeline, Revenue, Retention, and Expansion

•

Translate business processes into durable analytical schemas rather than report‑specific logic

•

Standardize event, object, and metric definitions across systems

•

Support complex revenue use cases such as lead attribution, forecasting, cohort analysis, funnel conversion, and lifecycle reporting

•

Ensure data models support both historical accuracy and forward‑looking analysis

BI Enablement & Semantic Layer

•

Define how transformed data is exposed to BI tools such as Power BI and Tableau

•

Build and maintain a governed semantic layer to enable self‑service analytics

•

Ensure dashboards are powered by consistent, reusable data models rather than embedded logic

•

Reduce duplication, manual calculations, and ad‑hoc reporting debt across the organization

•

Ensure seamless reconciliation capability between ERP & CRM data, managed through BI

Data Quality, Governance & Reliability

•

Establish data quality checks, validation rules, and reconciliation processes

•

Own metric governance in partnership with RevOps and Finance

•

Implement documentation, change control, and versioning for core datasets

•

Act as a point of accountability for GTM data correctness and consistency

Automation & Advanced Analytics Enablement

•

Eliminate manual reporting and spreadsheet‑based workflows through engineering solutions

•

Enable downstream use cases such as forecasting models, anomaly detection, and AI‑driven insights

•

Ensure data structures are suitable for experimentation and advanced analytics

Team Leadership

•

Lead and develop a small team of analytics engineers and senior analysts

•

Set engineering standards for data modeling, pipeline development, testing, and documentation

•

Balance hands‑on delivery with team growth and backlog prioritization

•

Build strong collaboration with RevOps, Finance, IT, and central Data teams

Stakeholder Partnership

•

Work closely with GTM and Finance leaders to translate analytical needs into data architecture decisions

•

Provide technical guidance on what is feasible, scalable, and sustainable

•

Support executive reporting by ensuring the underlying data foundation is sound

Experience

•

7–10+ years experience in data engineering, analytics engineering, or advanced BI roles

•

Proven experience designing and maintaining a