Senior Manager GTM Data Analytics

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 compensatio