Business Intelligence Engineer, Key
Amazon · Sunnyvale, California, USA ·
- Category
- Data analytics
agentic-aiaianalyticsbusiness-intelligencedata-governanceetlmysqlpythonredshiftroot-cause-analysissqltableauunix
Come join the Amazon Key for Business team and help drive the transformation of our customer delivery experience at scale. Key for Business, our patented building access technology, supports Amazon Last Mile across the US. Ring SMB extends this ecosystem to small and medium businesses with security, access control, and subscription-based services.
We are looking for a Business Intelligence Engineer to join our Key Data & BI team and help inform the direction of Ring SMB and Key for Business through data, at scale. This role spans two high-impact business verticals — Ring SMB and KfB NA-Multifamily — and sits at the center of the team’s AI-first transformation, where production AI agents, automated ETL workflows, and AI-assisted analytics are how work gets done today.
Successful candidates will be comfortable creating and optimizing table structures, identifying KPIs that drive the business, generating reports consumed across the organization, analyzing data to inform strategic and tactical initiatives, and writing up findings for less-technical audiences. Strong SQL is a must. AI fluency, scripting skills, and a background in operations or product analytics are extremely valuable.
This is a highly visible role that partners with business, product management, operations, finance, marketing, and engineering teams across Key for Business and Ring SMB
Key job responsibilities
• Design, implement, and support data platforms providing ad hoc and scheduled access to large datasets
• Manipulate and mine data from database tables (Redshift, MySQL, QuickSight SPICE) using SQL and ETL pipelines; write scripts (Python, UNIX) to process log files and telemetry data at scale.
• Develop and own analytical tools including deep-dive tools, dashboards (QuickSight, Tableau), Page 0 reporting surfaces, and executive-ready metric visualizations.
• Own and deliver weekly (WBR), monthly (MBR), and quarterly (QBR) executive reporting with data pulls, narrative insights, and metric deep dives
• Monitor existing metrics, develop and propose new metrics, and identify actionable risk signals
• Lead LTV analyses to inform promotional strategy, campaign measurement, and business decisions
• Perform deep-dives and root cause analysis to answer business questions; complete ad hoc data retrieval and analysis for product, finance, marketing, and operations stakeholders.
• Work with cross-functional stakeholders to define the source of truth for business metrics and obsessively ensure data fidelity across verticals.
• Effectively communicate (written and verbal) analytical findings, solutions, and methodologies to senior leadership and less-technical audiences.
• Work with engineering teams to enable appropriate capture and storage of data, including PII/non-PII data governance and compliance.
• Create and contribute to the team’s production AI agent ecosystem by feeding accurate datasets, metric definitions, and knowledge base content to agents
• Support metric framework development and provide foundational BI signals
• Leverage and extend AI-assisted workflows for automated ETL, TextQL for ad-hoc analysis, and the Skill Markdown Methodology for encoding domain knowledge into AI agents.
We are looking for a Business Intelligence Engineer to join our Key Data & BI team and help inform the direction of Ring SMB and Key for Business through data, at scale. This role spans two high-impact business verticals — Ring SMB and KfB NA-Multifamily — and sits at the center of the team’s AI-first transformation, where production AI agents, automated ETL workflows, and AI-assisted analytics are how work gets done today.
Successful candidates will be comfortable creating and optimizing table structures, identifying KPIs that drive the business, generating reports consumed across the organization, analyzing data to inform strategic and tactical initiatives, and writing up findings for less-technical audiences. Strong SQL is a must. AI fluency, scripting skills, and a background in operations or product analytics are extremely valuable.
This is a highly visible role that partners with business, product management, operations, finance, marketing, and engineering teams across Key for Business and Ring SMB
Key job responsibilities
• Design, implement, and support data platforms providing ad hoc and scheduled access to large datasets
• Manipulate and mine data from database tables (Redshift, MySQL, QuickSight SPICE) using SQL and ETL pipelines; write scripts (Python, UNIX) to process log files and telemetry data at scale.
• Develop and own analytical tools including deep-dive tools, dashboards (QuickSight, Tableau), Page 0 reporting surfaces, and executive-ready metric visualizations.
• Own and deliver weekly (WBR), monthly (MBR), and quarterly (QBR) executive reporting with data pulls, narrative insights, and metric deep dives
• Monitor existing metrics, develop and propose new metrics, and identify actionable risk signals
• Lead LTV analyses to inform promotional strategy, campaign measurement, and business decisions
• Perform deep-dives and root cause analysis to answer business questions; complete ad hoc data retrieval and analysis for product, finance, marketing, and operations stakeholders.
• Work with cross-functional stakeholders to define the source of truth for business metrics and obsessively ensure data fidelity across verticals.
• Effectively communicate (written and verbal) analytical findings, solutions, and methodologies to senior leadership and less-technical audiences.
• Work with engineering teams to enable appropriate capture and storage of data, including PII/non-PII data governance and compliance.
• Create and contribute to the team’s production AI agent ecosystem by feeding accurate datasets, metric definitions, and knowledge base content to agents
• Support metric framework development and provide foundational BI signals
• Leverage and extend AI-assisted workflows for automated ETL, TextQL for ad-hoc analysis, and the Skill Markdown Methodology for encoding domain knowledge into AI agents.