Senior Manager GTM Data Analytics & Engineering in London

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 analytical data architectures, including data cube/data warehousing setup.
• Experience owning SQL‑based transformation layers and data models at scale.
• Experience integrating multiple SaaS systems & tools into a unified analytical environment.
• Experience working with GTM, revenue, or commercial data in a SaaS business with appropriate commercial acumen, capable of understanding the key drivers & priorities of different GTM teams including Marketing, Sales, Customer Success, Renewals, etc.
• Experience leading & coaching technical analytics or data teams for both career development and big‑picture understanding.

Required Skills

• Advanced SQL and data modeling expertise.
• Strong understanding of ELT/ETL concepts and pipeline reliability.
• Experience working with modern analytics stacks and cloud data platforms.
• Strong understanding of revenue and GTM data structures.
• Ability to balance business requirements with long‑term architectural integrity.
• Clear, pragmatic communication with technical and non‑technical stakeholders.

Preferred Qualifications

• Background in Analytics Engineering (dbt‑style modeling approaches).
• Experience supporting revenue forecasting or financial reporting models.
• Experience operating in multi‑region SaaS environments.
• Exposure to AI‑enabled analytics or ML‑ready data architectures.

What we offer

• Onsite Onboarding in our HQ office for an optimal start.
• Great compensation and benefits packages including company achievement bonus or sales bonus, company stocks and regular salary reviews.
• Premiums for the private pension plan (BAV) up to the maximum amount are topped up by TeamViewer.
• Public transport friendly offices.
• Option to lease an e‑bike (Germany only).
• Special terms for local gyms.
• Access to Corporate Benefits platform with many discounts.
• Regular Team events and company‑wide celebrations.
• Open door policy, no dress code rules, frequent all Hands and Leadership Lunches.
• Hybrid and Flexible work time with up to 50% home office.
• Work From Abroad Program allowing up to 40 days of work outside your contracting country.
• We celebrate diversity as one of core values, join and drive one of the c‑a‑r‑e initiatives together with us!

TeamViewer is an equal opportunities employer and is committed to building an inclusive culture where everyone feels welcome and supported. We C‑A‑R‑E and understand that our diverse, values‑driven culture makes us stronger. As we continue to grow as a company, we also focus on enabling our employees to grow both personally and professionally. We are proud to have an open and embracing workplace environment that will empower you to be your best no matter your gender, civil or family status, sexual orientation, religion, age, disability, education level, or race.