Amazon is hiring: Business Intelligence Engineer, Advertising Trust Data in Seat

As a Senior Business Intelligence Engineer in the Advertising Trust Data team at Amazon, your responsibilities will involve developing a deep understanding of various advertising data sources, conducting in-depth analyses, and translating requirements into effective data visualizations. You will proactively identify analyses to enhance customer trust and partner with other tech teams to execute analytics projects. This multifaceted role aims to improve the advertising experience on Amazon for both internal and external stakeholders.

To qualify for the Senior Business Intelligence Engineer position with Amazon, candidates should have at least 3 years of experience in analyzing and interpreting data across platforms like Redshift and Oracle. Proficient knowledge of data visualization tools such as Tableau or Quicksight, as well as experience in data modeling and building ETL pipelines, are also essential. Strong SQL skills and familiarity with statistical analysis tools will set you on the path to success in this role.

Amazon seeks candidates for the Senior Business Intelligence Engineer role who are self-starters and possess strong technical acumen. Ideal candidates should be comfortable diving deep into data and making data-driven decisions that prioritize customer focus. Experience with AWS solutions, data mining, and managing complex datasets in business environments are also highly valued to ensure you can thrive in our dynamic workplace.

Customer trust is paramount in the Senior Business Intelligence Engineer position at Amazon. The Advertising Trust Data team is dedicated to creating systems and policies that uphold this trust. In this role, your analyses and insights will play a crucial part in enhancing customer understanding and ensuring that advertising efforts align with regulatory and cultural expectations across various markets.

As a Senior Business Intelligence Engineer in Amazon's Advertising Trust Data team, you can expect a dynamic and inclusive work environment that fosters innovation and collaboration. You'll work alongside diverse teams, utilizing cutting-edge technologies and methodologies to drive insights that matter. Amazon's commitment to employee diversity and career growth ensures that you're not just another employee but an integral part of a vibrant corporate culture.

It's crucial to know your audience when creating data visualizations. I focus on simplifying complex datasets by using clear, concise visual elements and storytelling techniques. This means choosing the right chart types, ensuring a logical flow of information, and highlighting key takeaways in a visual format to facilitate decision-making.

Certainly! In one instance, I tackled a project involving disparate data sources where the goal was to enhance user engagement metrics. I developed an ETL pipeline to integrate data and provided visualizations for various teams. The hardest part was resolving data quality issues, but collaborating cross-functionally helped streamline our approach and led to actionable insights that improved engagement strategies.

I prioritize projects based on their impact on business goals and stakeholder urgency. I often use a matrix to assess and rank projects, which allows me to communicate effectively with my team about timelines and deliverables. Regular check-ins with stakeholders also help to adjust priorities as needed, ensuring we stay aligned with company objectives.

I have extensive experience writing complex SQL queries for various data analysis tasks. I often use SQL to extract data from large databases and create custom reports. My understanding of database structure allows me to optimize queries for performance, ensuring that I can efficiently pull relevant information for analysis.

In a previous role, I noticed a trend in customer purchasing behavior during specific seasonal promotions. By analyzing sales data, I was able to provide insights that guided our marketing strategy for future campaigns. This analysis not only improved our promotional success but also shaped our approach to inventory management, leading to a significant increase in sales during peak seasons.

I always implement validation processes that check for data accuracy at each stage of the analysis. This includes setting up automated alerts for discrepancies and reviewing data from different sources for consistency. Before finalizing reports, I conduct thorough quality checks and involve team members for feedback, ensuring we maintain high standards of data integrity.

I am proficient in using Tableau and Quicksight for data visualization and prefer SQL-based tools for data extraction and analysis. Python is also a favorite of mine for scripting and automating tasks. Each tool has its strengths, and I choose based on the specific needs of the project to ensure optimal results.

I actively follow industry blogs, attend webinars, and participate in conferences focused on data analytics and BI. Networking with other professionals in this space allows me to exchange knowledge and discover new trends and tools. Continuous learning is essential in this fast-evolving field, and I make it a point to allocate time for professional development.

I have collaborated closely with product, engineering, and marketing teams on various projects. I believe communication is key in these collaborations. Regular meetings and updates help align our objectives and discuss insights derived from our datasets. Understanding different perspectives enables us to create more effective solutions that address the needs of various stakeholders.

I am driven by the challenge of turning complex data into insightful stories that help businesses make informed decisions. Seeing my analyses lead to tangible improvements is incredibly rewarding. I am also motivated by the collaborative environment at companies like Amazon, where innovative ideas thrive and contribute to customer satisfaction and business growth.