Why This Job is Featured on The SaaS Jobs
This Analytics Engineer role stands out in a SaaS context because it sits at the intersection of revenue-facing teams and a centralized data function. Supporting Customer Success and Sales while drawing from multiple data sources reflects a subscription business reality where retention, expansion, and pipeline quality are measurable levers. The structure described, with global analytics hubs and US overlap hours from Argentina, also signals a mature, distributed operating model common in scaled SaaS organizations.
From a SaaS career perspective, the remit builds durable skills in stakeholder-driven analytics, data modeling requirements, and the shift from ad hoc reporting to self-serve, automated dashboards. Exposure to both descriptive and predictive work, including hypothesis testing and machine learning models tied to costs, forecasting, and product experience, maps well to how modern SaaS teams instrument decisions. Collaboration with data engineering on curated datasets reinforces experience that transfers across BI, analytics engineering, and product analytics tracks.
The role is best suited to someone who enjoys translating business questions into dependable metrics and is comfortable working across functions rather than within a single domain. It fits professionals who value clear ownership, can operate with distributed partners, and prefer a balance of hands-on analysis and systems-building that improves how teams consume data over time.
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.
