Business Intelligence Engineer - LATAM

As a Business Intelligence Engineer at Panoptyc, your primary responsibilities include designing and implementing data pipelines to extract, transform, and load data into data warehouses, developing scalable data models, and optimizing query performance for business intelligence applications. You'll also create and maintain BI processes using various programming languages and data engineering tools, and build automated data validation frameworks to ensure data quality and integrity.

To qualify for the Business Intelligence Engineer role at Panoptyc, you should have a minimum of 3 years of experience in data engineering or BI development, strong programming skills in Python and SQL, and experience with ETL development and data pipeline orchestration tools. Familiarity with cloud data warehouses like Snowflake, BigQuery, or Redshift is essential, along with a demonstrated ability to optimize data models and query performance at scale.

At Panoptyc, Business Intelligence Engineers utilize a variety of tools and technologies including Python, SQL, dbt for programming, and Airflow, Prefect, and Fivetran for ETL and data pipelines. They also work with cloud data warehousing solutions such as Snowflake, BigQuery, and Redshift, and employ data visualization tools like Power BI to present insights effectively.

The Business Intelligence Engineer role at Panoptyc is designed for full-time commitment, requiring you to work 40 hours per week. It's important to note that you need to have availability during US Eastern Standard Time (EST), allowing for real-time collaboration with the team.

Panoptyc is committed to supporting its remote employees through flexible work arrangements and fostering a dynamic global team environment. Employees have access to competitive salary and benefits while working in a fully remote setting, allowing for a healthy work-life balance and collaboration across geographic boundaries.

When answering this question, discuss specific examples of data pipelines you've designed and the technologies used. Include details about the challenges faced, how you optimized the pipelines, and the outcomes achieved, showcasing your expertise in architectures that drive data-driven insights.

In your response, share specific strategies you've utilized to optimize SQL queries, such as indexing, query rewriting, or utilizing execution plans. Provide examples of situations where your optimizations led to significant performance improvements, demonstrating your analytical skills.

Discuss your hands-on experience with ETL tools like Airflow, Prefect, or Fivetran. Explain the processes you followed to ensure data integrity, including data validation frameworks you created or utilized to maintain high-quality data throughout the ETL cycle.

Highlight a specific project where your collaboration with data scientists was crucial for success. Emphasize how you combined your BI engineering skills with their data science expertise to create impactful data solutions. Discuss the outcomes and any significant contributions you made.

Tailor your answer by detailing your preferred programming languages like Python and SQL, and discuss any data processing frameworks you’re familiar with. Explain why these tools work well in your experience, highlighting instances when you leveraged them effectively for BI tasks.

Provide a structured approach to troubleshooting, emphasizing critical thinking and problem-solving. Share examples where your methodical diagnosis of issues in data pipelines or models led to successful resolution and improved functionality.

Discuss your understanding of data governance principles, including data security, compliance, and data quality. Share examples of frameworks or practices you've implemented to uphold data governance within your projects, demonstrating your dedication to ethical data handling.

Explain your approach to continuous learning, including following industry blogs, attending webinars, participating in forums, and experimenting with new tools. Highlight your commitment to staying informed about advancements in BI and how you've applied new knowledge to improve your work.

Elaborate on your experience with various cloud data warehousing solutions like Snowflake, BigQuery, and Redshift. Provide specific examples of projects where you've leveraged these tools and how they contributed to improved data management and retrieval processes.

Share your strategies for bridging the technical gap when working with non-technical stakeholders. Discuss the importance of using straightforward language, visual aids, and analogies to convey complex concepts, while ensuring stakeholders understand the business implications of BI insights.