Analytical Engineering
Transforms raw revenue data into clean, trusted models that power reporting, forecasting, and decision-making across the GTM organisation.
Common Tools
The tech stack for this discipline
Fivetran
ETL/ELTA fully managed cloud ELT platform with 500+ pre-built connectors that automatically replicates data from SaaS applications, databases, and files into a destination data warehouse.
Used for
RevOps teams use Fivetran to automate the ingestion of Salesforce, HubSpot, Marketo, and other revenue data into Snowflake or BigQuery without building or maintaining custom ETL pipelines.
Airbyte
ETL/ELTAn open-source ELT platform with a large community-maintained connector library that allows organizations to replicate data from hundreds of sources into data warehouses and lakes.
Used for
RevOps and data engineering teams use Airbyte as a cost-effective, self-hostable alternative to Fivetran for building data pipelines from CRM, marketing, and product sources into their analytical warehouse.
BigQuery
Data WarehouseGoogle Cloud's serverless, highly-scalable data warehouse that uses SQL and supports petabyte-scale analytics with built-in machine learning capabilities via BigQuery ML.
Used for
RevOps teams at Google Cloud-native organizations use BigQuery to warehouse event streams, marketing touchpoints, and CRM exports for large-scale pipeline and attribution analysis without managing infrastructure.
dbt
ETL/ELTAn open-source transformation framework that enables analysts and engineers to define, test, and document data models using SQL within a version-controlled workflow.
Used for
RevOps and data teams use dbt to build clean, tested, documented transformation layers in the warehouse — converting raw CRM, marketing, and product data into reliable business metrics that power dashboards and models.
Snowflake
Data WarehouseA cloud-native data warehouse platform that separates storage from compute, enabling organizations to store and query massive volumes of structured and semi-structured data across multiple cloud providers.
Used for
RevOps teams use Snowflake as the central analytical layer where CRM, marketing, product, and financial data converge, enabling cross-functional revenue analytics that no single source system can provide.
Sigma
BI/AnalyticsA cloud-native BI platform that gives business analysts a spreadsheet-like interface directly on top of a cloud data warehouse, enabling familiar analysis at warehouse scale.
Used for
RevOps teams use Sigma to let revenue analysts explore warehouse-scale data with Excel-like ease — building ad-hoc analyses, deal-level inspections, and financial models without writing SQL or waiting for BI team queues.
Monte Carlo
Data QualityA data observability platform that monitors data pipelines and warehouses for anomalies — freshness issues, schema changes, volume drops, and distribution shifts — alerting teams before bad data reaches dashboards.
Used for
RevOps and data engineering teams use Monte Carlo to monitor their revenue data pipelines for failures or anomalies, ensuring that CRM syncs, attribution models, and forecast dashboards are always powered by reliable data.
Python
OtherThe dominant general-purpose programming language used in data science, automation, and analytics, with a rich ecosystem of libraries (pandas, scikit-learn, SQLAlchemy) essential for advanced RevOps work.
Used for
RevOps practitioners use Python to build data pipelines, automate reporting, clean and transform CRM data at scale, build predictive models for churn and lead scoring, and create custom integrations between revenue systems.
Tableau
BI/AnalyticsAn enterprise business intelligence and data visualization platform that enables users to build interactive dashboards and reports by connecting to virtually any data source.
Used for
RevOps teams use Tableau to build executive pipeline dashboards, cohort analyses, and forecast views by connecting directly to Salesforce or warehouse data, enabling self-service analytics across revenue functions.
Looker
BI/AnalyticsA Google Cloud business intelligence platform built around a semantic layer (LookML) that defines business metrics once and makes them consistent across all reports and data consumers.
Used for
RevOps teams use Looker to define canonical revenue metrics (ARR, pipeline coverage, win rate) in LookML once, ensuring every team across the business uses the same definitions when querying revenue data.
Core Skills
What practitioners do day-to-day
Data Modelling
TechnicalThe ability to design logical representations of business data — defining entities, relationships, grain, and hierarchies — that accurately represent revenue processes and support reliable analytics.
Business outcome
Well-designed data models ensure metrics are calculated consistently, data is non-duplicated, and analytical queries run accurately — giving every team a shared, trustworthy view of revenue.
Data Governance
ProcessThe practice of establishing and enforcing policies, standards, ownership, and processes that ensure revenue data is accurate, consistent, complete, and accessible across systems and teams.
Business outcome
Strong data governance ensures that all teams trust and align on the same revenue numbers — eliminating the spreadsheet discrepancy problem that wastes time in every cross-functional revenue review.
Data Pipeline Design
TechnicalThe ability to architect and build reliable data movement and transformation workflows that extract data from source systems, load it into a warehouse, and transform it into analytics-ready models.
Business outcome
Reliable data pipelines ensure all revenue dashboards reflect accurate, timely data — enabling confident forecasting, attribution, and performance management decisions.
ETL/ELT Design
TechnicalThe ability to architect and implement data extraction, loading, and transformation workflows — selecting appropriate tools, managing dependencies, and ensuring data arrives in the warehouse reliably and on schedule.
Business outcome
Reliable ELT/ETL design ensures revenue data arrives in analytics systems on time and in the correct form — the invisible infrastructure that makes all forecasting, attribution, and performance management possible.
SQL
TechnicalProficiency in Structured Query Language for querying, transforming, and analysing data stored in relational databases and cloud warehouses — the most fundamental analytical skill in RevOps.
Business outcome
SQL fluency allows RevOps practitioners to answer any data question independently — without depending on a data team — enabling faster, more accurate decision-making across all revenue functions.
Dashboard Design
AnalyticalThe skill of designing clear, actionable visual dashboards that translate complex revenue data into insights decision-makers can act on — covering layout, chart selection, metric hierarchy, and interactivity.
Business outcome
Well-designed dashboards reduce the time revenue leaders spend in data extraction and preparation, enabling faster decision cycles, more consistent review rhythms, and broader data literacy across the org.