Why This Job is Featured on The SaaS Jobs
Analytics Engineering sits at a pivotal junction in SaaS: translating messy, multi-system operational data into trusted, decision-ready datasets. This listing is notable because the remit spans core SaaS functions—Sales and Customer Success first, with scope to support other departments—reflecting how subscription businesses rely on consistent measurement across the full customer lifecycle. The emphasis on automation and data quality signals a role that influences how teams define and monitor recurring-revenue performance, not just one-off reporting.
For a long-term SaaS career, the work builds durable leverage: stakeholder partnership to define metrics, data modeling that standardizes business logic, and scalable self-serve analytics that reduces ad-hoc dependence. Exposure to predictive methods and hypothesis testing also aligns with how mature SaaS organizations move from descriptive dashboards to forecasting and optimization. Operating within a centralized data team that serves multiple global hubs adds experience in cross-region collaboration and analytics delivery at enterprise cadence.
This role suits an analytics professional who prefers structured problem-solving and clear ownership of end-to-end outcomes—from requirements to dashboards to statistical work. It will fit someone comfortable working across business and technical counterparts, and who values a schedule aligned to US overlap hours while being based in Buenos Aires.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Your role
As a Data Analyst, you will collaborate with business stakeholders across various departments to effectively leverage big data and provide accurate, actionable insights that address key business questions. This role is based in Argentina and reports to the Manager of Data Analytics in Argentina. The candidate must be available to work during US overlapping hours (12 NN to 8 PM ART) and be willing to travel occasionally.
Dialpad’s data team acts as a centralized hub, supporting the analytics and business intelligence needs of all global departments such as Finance, Product, Engineering, Customer Support, Marketing, Legal, HR, and Sales. The team is responsible for data ingestion, ensuring data quality, managing data engineering, and delivering business analytics services, including report automation, as well as descriptive and predictive analytics. Dialpad’s data team operates from five global hubs located in the US, Canada, Argentina, India, and the Philippines.
What you’ll do
- Collaborate with Customer Success & Sales teams to analyze data from multiple sources, delivering clear data insights that enable informed, strategic decisions.
- Initially support the Customer Success & Sales teams, with flexibility to assist other departments as needs arise.
- Promote self-serve analytics throughout the organization by developing a comprehensive suite of fully automated, accurate, and reliable dashboards.
- Develop detailed data model requirements and document business logic for the Data Engineering team to create curated datasets for report automation and analysis.
- Perform statistical analysis, hypothesis testing, and build predictive machine learning models to optimize costs, forecast business outcomes, enhance product experiences, and drive business growth.
- Collaborate with external teams to improve the quality and usability of our internal data.
- Proactively monitor business performance through automated dashboards and ad-hoc analysis, providing leadership with actionable insights.
- Collaborate with data analysts and engineers across the organization to improve best practices and enhance data utilization and processes.
- Identify opportunities for continuous process improvement and cost optimization.
Skills you’ll bring
- 5+ years of advanced data analytics experience using big data.
- Experience working directly with business stakeholders to define requirements, drive analytics, and deliver effective insights.
- Experience collaborating with Data Engineers, Business Analysts, and Team Leads, with a demonstrated track record of successfully working with offshore teams and delivering complex projects on time.
- Advanced SQL development skills for data exploration and analysis.
- Strong analytical and problem-solving skills.
- Experience building effective visualizations and dashboard automations in an advanced BI tool like Tableau (preferred), PowerBI, DOMO, or Looker.
- Bachelor’s degree in a technical or business field.
- Expertise in product and financial data analysis.
- Experience working in a SaaS-based technology company.
- Experience in building and training machine learning models for business analysis use cases.
- Proficiency in Python for data exploration and analysis.
- Hands-on experience with Agile Software Development methodologies and tools like Jira.
- Working experience in cloud-based data platforms like GCP, AWS, or Azure.
