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
What you'll do
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.
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 champion, evangelizing and guiding business users on the most efficient and reliable ways to leverage our data products, accelerating their time to insights
Emerging Technology & Platform Innovation:
Lead the research and evaluation of new tools and technologies, like GenAI, for enhancing data engineering, orchestration, and analysis workflows.
Develop and test high-impact prototypes that demonstrate the potential of emerging technologies (e.g., GenAI) to augment and improve our product usage datasets and data platform capabilities.
Requirements
2+ years of experience in an Analytics Engineering or Data Warehousing role.
Expert proficiency in SQL, including advanced techniques like window functions and proven ability in query performance optimization.
Demonstrated expertise in dbt (Data Build Tool) for designing, developing, and maintaining complex data models, coupled with strong functional knowledge of a modern cloud data warehouse (e.g., Snowflake, BigQuery).
Proven ability to apply data engineering best practices, including version control (Git/GitHub), modular coding, and automated testing, to maintain robust and reliable data pipelines.
Strong understanding of data modeling principles (e.g., star/snowflake schemas, Slowly Changing Dimensions) and how to apply them to solve analytical business problems.
Proficiency in Python or another scripting language is required.
Experience with data orchestration tools (e.g., Airflow, Dagster) for building and managing data workflows.
Soft Skills / Personal Characteristics
Resourceful, results-oriented, and autonomous, with a proven track record of owning the full lifecycle of analytical projects from ambiguous requirements to final delivery and business impact.
Excellent verbal and written communication and stakeholder management skills, with the ability to translate complex data logic for non-technical audiences and effectively drive cross-functional alignment.