Senior Data Engineer in Analytics

As the Senior Data Engineer in Analytics at Simbe, your primary responsibilities include designing and scaling the analytics infrastructure, ingesting data from various sources into BigQuery, transforming raw data using dbt into accessible ‘data-marts’ for business intelligence tools, and ensuring data quality and governance through data testing frameworks. You will collaborate with multiple teams to champion DataOps best practices and evaluate tools that address evolving business needs.

To excel as the Senior Data Engineer in Analytics at Simbe, you should ideally possess a bachelor’s or master’s degree in Computer Science (or a related field) and have at least three years of hands-on experience with SQL, BigQuery, and Google Cloud Platform (GCP). Proficiency with dbt and experience with Postgres are highly preferred, alongside familiarity with APIs and data streaming technologies.

In the role of Senior Data Engineer, you will leverage dbt to set up a strong data testing framework that ensures the integrity and accuracy of data. This includes drafting a comprehensive suite of metric definitions and documentation accessible organization-wide. Additionally, you will implement controls that guarantee compliance with regulations such as GDPR and SOC 2, ensuring that data security and governance are a top priority.

The Senior Data Engineer at Simbe primarily works with tools like BigQuery for data warehousing and dbt for transforming raw data. Experience with PostgreSQL, Google Cloud Storage, and APIs for data ingestion is crucial. Familiarity with tools for monitoring cloud costs and ensuring effective data governance will also play a significant role in your responsibilities.

At Simbe, the work environment is dynamic and inclusive, aimed at fostering innovation in the retail space. As a Senior Data Engineer in Analytics, you will collaborate with cross-functional teams while championing best practices in DataOps. Our culture emphasizes open communication, accountability, and a passion for technology, making it a vibrant space to grow and succeed.

In answering this question, it's beneficial to outline specific projects you’ve worked on that involved SQL and BigQuery. Discuss how you utilized SQL to craft queries that informed business decisions, as well as any complex data transformations you implemented using BigQuery, emphasizing your understanding of data warehousing principles.

When tackling this question, define DataOps as a set of practices that combines data engineering, analytics, and operations, aiming to improve the speed and quality of data analytics. Illustrate how you would advocate for DataOps practices within teams, such as automating data testing and fostering collaboration among data engineers, analysts, and scientists.

Choose a specific example and take the interviewer through your thought process. Highlight the complexities of the problem, the steps you took to analyze and resolve it, and the impact of your solution. This will showcase your analytical thinking and problem-solving skills in real-world scenarios.

You should detail the methods you employ to maintain data quality, including controlling data ingestion processes, conducting data validations, and using monitoring tools. Stress the importance of automated tests and regular audits to catch discrepancies early, ensuring that high-quality data is available for analysis.

In your response, discuss practical strategies like regularly analyzing data storage requirements, implementing cost-monitoring tools, and optimizing queries. Detail how you'd identify unnecessary data retention and apply efficient data partitioning to reduce costs while maintaining effective performance.

Address how effective communication strategies, setting clear expectations, and maintaining an open-door policy can foster collaboration. Share experiences where you’ve successfully worked with diverse teams, promoting a culture of inclusivity, understanding, and aligned goals.

Here, you should cite specific governance frameworks that you have established or improved. Discuss how you’ve contributed to creating documentation, define roles in data management, or how you’ve enforced data access policies to ensure compliance and security across the organization.

Explain that documentation is crucial for maintaining clarity and continuity in data workflows. You should mention specific types of documentation—such as data flow diagrams, schema definitions, or process guides—and how they help onboard new team members, facilitate audits, and promote transparency.

When discussing this, mention the criteria you would consider, such as scalability, ease of integration with existing systems, user-friendliness, and community support. Talk about how you would gather feedback from stakeholders and potentially pilot tools to ensure they meet business needs before full implementation.

Focus on the principles of normalization versus denormalization, understanding the end-user needs, and ensuring the data model supports analytical queries effectively. Discuss balancing performance, flexibility, and maintaining the integrity of the data to provide a solid foundation for analytics.