Jobs / Singapore / Workato INC
Analytics Engineer
Workato INC · 🌍 Singapore
Sponsorship verdict
No sponsorship evidence yet
No government record and no wording either way. Not a refusal — ask the recruiter.
- No government sponsor record hereNo government sponsor record covers this employer in this country.
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- Can’t check pay against the visa rulesNo salary stated. Singapore Employment Pass needs at least SGD 67,200 a year. Source: https://www.mom.gov.sg/passes-and-permits/employment-pass/eligibility, rules effective 2025-01-01.
- Confirmed live todayWhen a source last listed this job as open.
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Sponsor Radar — Workato INC
No government sponsor record covers this employer in this country.
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About the role
About Workato Workato is the leading Control and Execution Platform for Enterprise AI — the neutral platform enterprises trust to put AI to work across their business. Workato unifies data, applications, and processes into a single platform so AI can reliably orchestrate business processes in production at enterprise scale. Built on more than a decade of running mission-critical processes for over half the Fortune 500 — including Nasdaq, Amazon, Cisco, Vodafone, Atlassian, and Lucid Motors — Workato turns over 14,000 enterprise systems AI needs to act on into one governed execution layer. For more information, visit workato.com . Why join us? Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles . We are driven by innovation and looking for team players who want to actively build our company. But, we also believe in balancing productivity with self-care . That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives. If this sounds right up your alley, please submit an application. We look forward to getting to know you! Also, feel free to check out why: • Business Insider named us an “enterprise startup to bet your career on” • Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world • Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America • Quartz ranked us the #1 best company for remote workers Responsibilities As an Analytics Engineer in the Product Management team, you will own the end-to-end delivery of robust, high-quality data products. You will be responsible for designing, developing, maintaining, and scaling mission-critical data models to provide reliable and accessible product usage data, proactively partnering with Data Engineers, Product Analysts, and business stakeholders. Your key mandate is to transform raw data into actionable insights that directly drive strategic product and business decisions, with a continuous focus on technical excellence and platform optimization. In this role, y ou will also be responsible to: • DBT Modeling & Scalability: • Design, develop, and own scalable and maintainable data models using dbt (Data Build Tool), ensuring accurate, intuitive, and consistent data for all end users and stakeholders. • Collaborate actively with Data Analysts and Business Stakeholders to translate complex reporting and analysis needs into production-ready, highly optimized dbt models. • Enforce and evolve our internal dbt conventions and best practices, continuously optimizing the codebase for cleanliness, performance, and cost-efficiency. • Data Reliability and Quality Assurance: • Own and enforce data quality and consistency by implementing robust testing, validation, and cleaning processes on mission-critical source tables. • Implement and manage data monitoring and alerting solutions to ensure data flows and transformations are performing optimally and accurately, and proactively resolve data anomalies and pipeline failures. • Create and maintain comprehensive data documentation and definitions (data dictionaries, process flows) to ensure data literacy, trust, and discoverability for stakeholders • Stakeholder Collaboration & Data Enablement: • Partner with data engineers, product analysts, GTM data teams, and other stakeholders to strategically align data insights with product improvements and business objectives. • Communicate complex data architecture, patterns, and analytical conclusions effectively to both technical and non-technical audiences, driving consensus and action. • Act as a data ch