Analytics Engineer

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.

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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.

• 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

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, you will also be responsible to:

• DBT Modeling & Scalability:
• Data Reliability and Quality Assurance:
• Stakeholder Collaboration & Data Enablement:
• Emerging Technology & Platform Innovation:

• 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, Big Query).
• Proven ability to apply data engineering best practices, including version control (Git/Git Hub), 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.

• 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.