Senior Analytics Engineer, Data Architecture & Governance

About the client

Our client is a leading provider of field service management software for trade contractors worldwide. It runs on a subscription model and is focused on scalable, data-driven growth. Its Data Team builds the trusted analytics foundation that supports decision-making across the organization.

About the role

We are looking for a Senior Analytics Engineer to join the Data Team as a senior individual contributor. This is a hands-on, highly business-facing role focused on building the trusted data architecture, models, documentation and business logic the company uses to make decisions. It is not a people-management role, and it is not a dashboard or visualization role.

You will work across Sales, Marketing, Finance, Revenue Operations and Product to understand how processes, systems, data and KPIs fit together, and turn that into scalable data models and clear definitions. The focus is go-to-market and subscription revenue data: pipeline, bookings, ARR, MRR, churn, retention, expansion and customer lifecycle. You will work hands-on with Google BigQuery, dbt and Fivetran, and you are expected to be AI-first, using approved AI tools securely to move faster without compromising data security, privacy or governance.

What you'll do

- Build and own core data models: design, build and maintain scalable models in BigQuery with dbt, creating certified datasets that downstream teams can trust.

- Connect the dots across the business: understand how processes, source systems, data flows and KPIs connect, and fix misaligned definitions and logic.

- Define and standardize business KPIs: ARR, MRR, churn, net and gross revenue retention, expansion, bookings, pipeline, conversion rates and lifecycle metrics.

- Model the sales and revenue funnel: from lead generation and marketing attribution through opportunities, pipeline, bookings, renewals, expansion and retention.

- Own documentation and data definitions: datasets, fields, transformations, business rules, metric definitions, lineage, assumptions and known limitations.

- Translate business logic into data architecture: clean SQL, scalable schemas and reusable dbt models, explaining tradeoffs when business rules or systems change.

- Strengthen data governance and quality: modeling standards, naming conventions, testing, validation and certification of production-ready datasets, resolving issues at the source.

- Use AI securely to move faster: automate repetitive work, accelerate documentation and data discovery, and validate logic, always under company governance standards.

- Partner with the Senior Data Architect and the Data Team on architecture decisions, dbt best practices, platform optimization and long-term data strategy.

What success looks like

- Trusted, certified, well-documented data models used consistently across teams.

- Key SaaS and revenue metrics that are standardized, traceable and understood by the business.

- Business users and reporting teams who can self-serve from clean, reliable datasets.

- Data quality issues caught early, documented and resolved with the right owners.

- A Data Team seen as the owner of trusted data architecture, not a one-off dashboard request team.

Requirements

Required

- 5+ years in analytics engineering, data analytics, data modeling, BI engineering or data architecture roles.

- Strong hands-on experience with Google BigQuery, including query optimization, partitioning, clustering, performance tuning and cost management.

- Advanced SQL: clean, efficient, scalable and well-documented.

- Hands-on dbt experience: modeling, testing, documentation, lineage and deployment.

- Experience with ELT pipelines and ingestion tools such as Fivetran.

- Experience in a SaaS, software, subscription or recurring revenue business.

- Strong understanding of SaaS metrics (ARR, MRR, churn, retention, expansion, bookings, pipeline) and of the B2B sales motion, from lead to renewal and expansion.

- Ability to connect business processes, source-system data and reporting needs into a coherent data model.

- Strong documentation discipline and the ability to explain complex data structures and metric definitions clearly.

- AI-first mindset, using approved AI tools securely while protecting data privacy and governance.

- Ability to work independently as a senior individual contributor with strong ownership and judgment.

- Comfortable working across time zones in a distributed global team, with advanced English.

Nice to have

- Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, Business, Information Systems or a related field.

- Experience with Salesforce data structures, CRM objects and revenue operations data.

- Experience with marketing attribution, lead funnel data and HubSpot or similar platforms.

- Experience with data governance, metadata management, data cataloging or certified dataset frameworks.

- Experience with Google Cloud tools such as Dataflow, Pub/Sub, Cloud Composer or Dataplex.

- Python for data transformation, automation, validation or schema management.

- Experience using AI-enabled tools in analytics engineering workflows (documentation, SQL, testing, data quality).

- Experience configuring or managing Fivetran connectors.

- Experience supporting Finance, RevOps, Sales Ops, Marketing Ops or executive reporting teams.

Benefits

- Permanent contract (contrato a término indefinido) with all legal benefits

- Competitive salary, commensurate with experience

- Private health insurance (medicina prepagada)

- Relocation bonus to Medellín

- On-site role in Medellín with parking

- The chance to build the core data foundation of a global software business