Analytics Engineer

About Workato

Workato is a leading Control and Execution Platform designed for Enterprise AI. Trusted by major companies including Nasdaq, Amazon, and Cisco, it consolidates over 14,000 enterprise systems into a unified, governed platform that enables AI to operationalize business processes at scale.

We foster a flexible, trust-driven culture that empowers ownership and innovation, balancing productivity with employee well-being. Workato is recognized by Business Insider, Forbes Cloud 100, Deloitte Tech Fast 500, and Quartz for our rapid growth and commitment to remote work excellence.

Key Responsibilities

• Develop and maintain scalable, high-quality data models using dbt, ensuring data accuracy and consistency for all stakeholders.
• Collaborate with data analysts and business teams to convert complex analytical requirements into optimized dbt models.
• Maintain and improve dbt coding standards and best practices to optimize performance and reduce costs.
• Implement rigorous data quality checks, testing, and monitoring to ensure reliable data pipelines and promptly address anomalies.
• Document data architectures, workflows, and definitions to enhance data clarity and accessibility for users.
• Work closely with data engineers, product analysts, and GTM data teams to align data insights with product and business goals.
• Effectively communicate complex data concepts to diverse audiences to facilitate informed decision-making and consensus.
• Promote data literacy and guide business users in utilizing data products efficiently to accelerate insight generation.
• Research and experiment with emerging technologies such as GenAI to advance data engineering and platform capabilities.

Qualifications and Skills

• Minimum of 2 years experience in analytics engineering or data warehousing roles.
• Advanced SQL skills, including window functions and query optimization techniques.
• Proven expertise with dbt for complex data modeling and familiarity with modern cloud data warehouses like Snowflake or BigQuery.
• Experience applying data engineering best practices, including version control via Git/GitHub, modular development, and automated testing.
• Solid understanding of data modeling concepts such as star/snowflake schemas and Slowly Changing Dimensions.
• Proficiency in Python or a comparable scripting language.
• Hands-on experience with data orchestration platforms like Airflow or Dagster.
• Strong problem-solving abilities, autonomy, and ownership over complete analytical projects.
• Exceptional communication and stakeholder management skills, capable of translating complex data logic for non-technical audiences.