Analytics Engineer

Навыки

• AI-агенты
• AI-инструменты в работе
• Airflow
• BigQuery
• Dagster / Prefect / Luigi
• Data Quality
• dbt

О компании и продукте

• 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
• 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!

Задачи

• 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, y ou will also be responsible to
• DBT Modeling & Scalability
• 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

Требования

• Qualifications / Experience / Technical Skills
• 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
• (REQ ID: 2867)

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