Analytics Engineer

Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. A leader in Enterprise MCP and trusted by 50% of the Fortune 500, Workato’s cloud-native architecture connects every application, data source, and process to power real-time orchestration at scale. With enterprise-grade security and continuous innovation at its core, Workato provides the trusted foundation for organizations to automate with confidence and operationalize AI across the business. To learn more, visit www.workato.com

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:

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• 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.

• 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

• 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

• 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.

• 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.

• 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.