Analytics Engineer

About Workato

Workato provides a cutting-edge enterprise platform that integrates data, applications, processes, and artificial intelligence into a unified, governed cloud-native infrastructure. Trusted by half of the Fortune 500, their innovative iPaaS solution offers real-time orchestration with enterprise-level security and constant innovation. Workato supports organizations in confident automation and widespread AI operationalization.

Workato values a flexible, trust-driven culture where employees have full ownership of their responsibilities. The company fosters innovation and teamwork while promoting a healthy work-life balance through a dynamic work environment and diverse benefits.

Recognition includes:

• Named by Business Insider as an “enterprise startup to bet your career on”
• Featured in Forbes’ Cloud 100 list of top private cloud firms globally
• Ranked 17th fastest growing tech company in Bay Area and 96th in North America by Deloitte Tech Fast 500
• Rated the #1 best company for remote workers by Quartz

Role Overview & Responsibilities

As an Analytics Engineer within the Product Management group, you will be responsible for the complete lifecycle of delivering high-quality, reliable data products. Your core task is to build, maintain, and scale essential data models that provide accessible product usage metrics, collaborating closely with data engineers, product analysts, and business partners. Your work will enable actionable insights that inform strategic product and business decisions while emphasizing technical excellence and platform efficiency.

Key responsibilities include:

• Utilize dbt for designing scalable, well-maintained data models that serve stakeholders with accurate and intuitive data.
• Translate complex reporting and analytical requirements into robust, optimized dbt models through collaboration with analysts and business teams.
• Continuously uphold and enhance dbt best practices for code quality, performance improvements, and cost-effective solutions.
• Ensure data reliability by enforcing data quality standards, conducting validations, cleaning data, and managing source table integrity.
• Develop and maintain data monitoring and alerting systems to promptly detect and address any pipeline issues.
• Create detailed documentation, including data dictionaries and process flows, to promote data understanding and accessibility.
• Coordinate with cross-functional teams to align data insights with product enhancements and business goals.
• Communicate complex data structures and analysis outcomes clearly to both technical and non-technical stakeholders, fostering actionable consensus.
• Serve as a data advocate by educating business users on leveraging data solutions efficiently to expedite insight generation.
• Lead exploration and pilot projects of emerging technologies, such as Generative AI, to enhance data engineering and analytic workflows.

Qualifications and Skills

• At least 2 years of experience in analytics engineering or data warehousing roles.
• Advanced SQL capability, including window functions and expertise in optimizing query performance.
• Proficiency in dbt for constructing and managing data models, with strong experience in cloud data warehouses like Snowflake or BigQuery.
• Solid application of data engineering best practices, including version control (Git), modular programming, and automated testing to ensure pipeline robustness.
• Comprehensive knowledge of data modeling techniques such as star and snowflake schemas and handling Slowly Changing Dimensions.
• Proficiency in scripting languages such as Python is required.
• Experience using data orchestration tools (e.g., Airflow, Dagster) for workflow management.

Soft Skills

• Highly resourceful and self-driven, capable of independently managing analytical projects from unclear beginnings to impactful delivery.
• Strong communication and stakeholder engagement skills, adept at translating complex data concepts to non-technical audiences and driving cross-team collaboration.