Senior Data Engineer

As a Senior Data Engineer at Workato, you will manage two crucial data circuits. One focuses on the raw data layer for internal analytics, collaborating with the Product and Business analytics teams, while the other supports in-product analytics for user insights. Your main responsibility will be to develop a usage tracking and billing platform offering accurate near real-time data for both circuits. You'll also work closely with the ML team, contributing to the foundation for future projects.

To step into the role of Senior Data Engineer at Workato, you should have over 5 years of experience building and maintaining data pipelines in data-centric environments. A fluent command of SQL, strong expertise in analytical programming languages like Java and Scala, and experience with tools like Flink and Spark are essential. Familiarity with data orchestration tools and data warehousing solutions is also required, along with good communication skills.

In the dynamic role of Senior Data Engineer at Workato, you’ll work with exciting technologies such as Iceberg Datalake, Snowflake, Clickhouse, Airflow, Flink, Spark, and the Kafka Ecosystem. Your experience with these tools will be crucial as you develop and modernize data pipelines and support advanced analytics capabilities.

As a Senior Data Engineer at Workato, your contributions will significantly impact how data is processed and utilized across our platform. By developing a robust usage tracking and billing platform, you’ll help optimize data-driven decisions and enhance overall user experience. Your collaboration with internal teams and your technical insights will also play a key role in shaping future analytics initiatives.

At Workato, the company culture prioritizes flexibility and trust, encouraging employees to take full ownership of their roles. We foster an innovative environment where team players can thrive while balancing productivity with self-care. Our vibrant workplace, alongside numerous employee benefits, ensures that everyone feels valued and empowered.

When answering this question, highlight your familiarity with tools like Airflow or Dagster. Discuss specific projects where you've implemented these tools, outlining the challenges faced, the solutions you developed, and the overall impact on the projects. Be ready to demonstrate your understanding of orchestration's role in data engineering.

Highlight your experience with performance tuning and the techniques you've employed to optimize data pipeline performance. Discuss aspects such as query optimization, resource allocation, and using caching mechanisms. Providing examples from your previous work will strengthen your response.

Address data privacy and security by discussing your understanding of regulations like GDPR and CCPA. Share your experience in implementing data protection measures in your projects, including data encryption, access controls, and conducting compliance audits to ensure data safety.

Focus on discussing any relevant experience you have in building similar platforms. Explain your approach to requirements gathering, technical design, and testing methods. Including the technologies and frameworks you used, as well as the integration processes, will give the interviewer a thorough understanding of your capability.

Discuss specific challenges such as scaling issues, handling streaming data, or optimizing resource usage in Spark or Flink. Explain how you approached and overcame these challenges, demonstrating your problem-solving skills and technical expertise.

Elaborate on strategies for maintaining data quality, such as implementing data validation rules, monitoring data accuracy, and conducting regular audits. Providing examples of how you’ve used testing and monitoring processes will showcase your thorough understanding of data integrity.

Share details about your hands-on experience with cloud data warehousing solutions. Discuss specific use cases and how you leveraged these platforms to enhance data accessibility and analysis. Include any performance tuning or cost optimization strategies you've employed.

Outline your approach to cross-team collaboration, emphasizing your communication skills. Discuss how you align objectives with stakeholders, gather requirements, and ensure transparency throughout project lifecycles. Provide examples of successful collaborations to illustrate your point.

Highlight the techniques you use for monitoring data pipelines, such as setting up alerts, utilizing monitoring tools, and implementing logging best practices. Discuss how consistent monitoring can preemptively address issues, maintaining pipeline health and efficiency.

Discuss your experience with version control using Git, emphasizing collaborative practices like code reviews, branching, and merge strategies. Share specific examples that demonstrate your ability to work collaboratively within a team, highlighting any successful outcomes from those practices.