Business Intelligence Engineer, Amazon Prime Video Product Analytics
for each section heading.
We need to use - for any list of duties/requirements/benefits. Use
for prose.
We must preserve ALL substantive information; do not invent or summarize away detail.
Let's extract content.
About the role: The first paragraph(s) describing Prime Video, PVPA team, mission. Let's capture:
"Prime Video is changing the way customers watch movies, TV shows, live events, and channels—offering unparalleled choice and convenience across devices such as Fire TV, mobile phones, game consoles, and connected TVs. We deliver world-class viewing experiences to customers in over 240 countries and territories. The Prime Video Product Analytics (PVPA) team is a centralized analytics organization supporting the Prime Video Product organization. We bring together Business Intelligence Engineers, Data Engineers, and Data Scientists to deliver comprehensive data tools and insights that drive product decisions and enhance customer experience. Our work spans the full analytics lifecycle—from data infrastructure and pipeline development to advanced analytics, customer insights modeling, AI-driven solutions, and self-service reporting. This role sits within the PVPA team focused on out-of-app engagement and full-funnel customer journey analytics. Our mission is to understand and optimize how customers discover, engage with, and return to Prime Video—connecting off-app marketing touchpoints (such as email, push, a.com, WhatsApp, search engines, and paid media) with in-app behavior and engagement to build a complete, end-to-end view of the customer experience. We harness data across this full funnel—from acquisition and onboarding through long-term retention—to inform personalization, measure customer engagement and channel effectiveness, and deliver the right content to the right customer at the right time."
That's substantive.
Responsibilities: bullet list from "Key job responsibilities" and "In This Role, You Will". Let's extract:
- Design and maintain scalable data models, pipelines, and reporting tools that enable accurate and timely marketing performance measurement across multiple customer engagement channels (e.g., email, push, X-site, WhatsApp, paid media).
- Lead the design, execution, and analysis of A/B tests to evaluate marketing strategies, targeting logic, and content variations—ensuring statistical rigor and actionable insights.
- Deliver deep customer insights by analyzing behavioral data and engagement patterns, helping stakeholder and partner teams better understand customer needs, preferences, and responses to messaging.
- Develop dashboards and self-service tools to enable marketers, product managers, and other stakeholders to explore data, monitor performance, and make informed decisions.
- Collaborate closely with cross-functional teams—including Data Scientists, Engineers, Product Managers, and Marketing Managers—to define metrics, surface insights, and influence strategy through data.
- Communicate findings clearly to both technical and non-technical stakeholders, using data storytelling to highlight opportunities, surface risks, and drive alignment on next steps.
- Champion data accuracy, consistency, and experimentation best practices to raise the overall bar for measurement and insight generation in Prime Video marketing.
Also earlier: "You will design and build scalable analytics solutions, self-service reporting tools, and experimentation frameworks, increasingly powered by AI and automation, that give stakeholders across product, marketing, and science a deep, reliable understanding of customer behavior." Might be duplicate but we can include as part of responsibilities.
We need to ensure we don't duplicate lines. We'll combine unique items.
Requirements: Basic Qualifications list. Let's extract each bullet:
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- 1+ years of SQL, ETL or Oracle experience
- 1+ years of processing large, multi-dimensional datasets from multiple sources experience
- 1+ years of performing statistical analysis experience
- 1+ years of developing automated reporting experience
- 3+ years of in the job offered or a related occupation experience (this seems weird; maybe "3+ years of experience in the job offered or a related occupation")
- 1+ years of using SQL, ETL (Extract, Transform, Load), or Oracle experience (duplicate of earlier)
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
We need to deduplicate: there are multiple SQL/E/Oracle lines. We'll combine into a concise list but must preserve all substantive info; we can combine similar but not lose detail. However we must not invent or summarize away detail; but we can combine similar items as long as we keep the substance. The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." Combining similar items could be considered summarizing? Might be okay if we keep the same meaning. Safer to list each distinct requirement as separate bullet, even if similar. But we should avoid duplicate lines exactly. We'll keep unique items.
Let's list unique:
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- 1+ years of SQL, ETL or Oracle experience
- 1+ years of processing large, multi-dimensional datasets from multiple sources
- 1+ years of performing statistical analysis
- 1+ years of developing automated reporting
- 3+ years of experience in the job offered or a related occupation
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
Note: "1+ years of using SQL, ETL (Extract, Transform, Load), or Oracle experience" is similar to second; we can drop duplicate.
Qualifications: Preferred Qualifications:
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
We could also include maybe other preferred but that's it.
Skills: Could be derived from tools: SQL, ETL, Redshift, Oracle, NoSQL, Tableau, Quicksight, R, SAS, Matlab, Python, AWS EC2, DynamoDB, S3, Redshift (again). But we might not need a separate Skills section if we already covered in Requirements/Qualifications. However instruction: we can include Skills section if there is real specific content. We have skills mentioned; we could make a Skills section listing technical skills. But we must not create thin sections. Might be okay to have Skills section summarizing tools. However we already have Requirements and Qualifications covering experience; Skills could be redundant. Safer to omit Skills section unless we have distinct skill list not covered elsewhere. The posting mentions "Experience with data visualization using Tableau, Quicksight, or similar tools", "Experience with data modeling, warehousing and building ETL pipelines", "Experience in Statistical Analysis packages such as R, SAS and Matlab", "Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling", "Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift". These are skills. Could be placed under Skills. But we already have Requirements and Qualifications covering experience; maybe we can combine them. However the instruction says "Prefer a few substantive sections over many thin ones." So we could merge skills into Requirements or Qualifications. But we need to follow the headings list: we can include only those headings that have real specific content. If we decide not to have a Skills section, we omit it.
Benefits: we have description: "Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave." Also mention sign-on payments and RSUs (these are compensation, not benefits). We'll include Benefits section with that list.
Pay: we have base salary range: "USA, WA, SEATTLE - 99,500.00 - 160,000.00 USD annually". Also mention sign-on payments and RSUs. We'll include Pay section with that.
Schedule: not present, omit.
Now we need to output HTML fragment only.
Structure:
About the role
... (the paragraph)
Responsibilities
- ...
...
Requirements
- ...
...
Qualifications
- ...
...
Benefits
- ...
...
Pay
...
Make sure not to include any extra text.
Now craft the content precisely.
About the role paragraph: combine the two paragraphs into one? Could be two but we can keep as one
with line breaks? Better to keep as one
but it's long; still okay.
Let's write:
Prime Video is changing the way customers watch movies, TV shows, live events, and channels—offering unparalleled choice and convenience across devices such as Fire TV, mobile phones, game consoles, and connected TVs. We deliver world-class viewing experiences to customers in over 240 countries and territories. The Prime Video Product Analytics (PVPA) team is a centralized analytics organization supporting the Prime Video Product organization. We bring together Business Intelligence Engineers, Data Engineers, and Data Scientists to deliver comprehensive data tools and insights that drive product decisions and enhance customer experience. Our work spans the full analytics lifecycle—from data infrastructure and pipeline development to advanced analytics, customer insights modeling, AI-driven solutions, and self-service reporting. This role sits within the PVPA team focused on out-of-app engagement and full-funnel customer journey analytics. Our mission is to understand and optimize how customers discover, engage with, and return to Prime Video
for prose. We must preserve ALL substantive information; do not invent or summarize away detail. Let's extract content. About the role: The first paragraph(s) describing Prime Video, PVPA team, mission. Let's capture: "Prime Video is changing the way customers watch movies, TV shows, live events, and channels—offering unparalleled choice and convenience across devices such as Fire TV, mobile phones, game consoles, and connected TVs. We deliver world-class viewing experiences to customers in over 240 countries and territories. The Prime Video Product Analytics (PVPA) team is a centralized analytics organization supporting the Prime Video Product organization. We bring together Business Intelligence Engineers, Data Engineers, and Data Scientists to deliver comprehensive data tools and insights that drive product decisions and enhance customer experience. Our work spans the full analytics lifecycle—from data infrastructure and pipeline development to advanced analytics, customer insights modeling, AI-driven solutions, and self-service reporting. This role sits within the PVPA team focused on out-of-app engagement and full-funnel customer journey analytics. Our mission is to understand and optimize how customers discover, engage with, and return to Prime Video—connecting off-app marketing touchpoints (such as email, push, a.com, WhatsApp, search engines, and paid media) with in-app behavior and engagement to build a complete, end-to-end view of the customer experience. We harness data across this full funnel—from acquisition and onboarding through long-term retention—to inform personalization, measure customer engagement and channel effectiveness, and deliver the right content to the right customer at the right time." That's substantive. Responsibilities: bullet list from "Key job responsibilities" and "In This Role, You Will". Let's extract: - Design and maintain scalable data models, pipelines, and reporting tools that enable accurate and timely marketing performance measurement across multiple customer engagement channels (e.g., email, push, X-site, WhatsApp, paid media). - Lead the design, execution, and analysis of A/B tests to evaluate marketing strategies, targeting logic, and content variations—ensuring statistical rigor and actionable insights. - Deliver deep customer insights by analyzing behavioral data and engagement patterns, helping stakeholder and partner teams better understand customer needs, preferences, and responses to messaging. - Develop dashboards and self-service tools to enable marketers, product managers, and other stakeholders to explore data, monitor performance, and make informed decisions. - Collaborate closely with cross-functional teams—including Data Scientists, Engineers, Product Managers, and Marketing Managers—to define metrics, surface insights, and influence strategy through data. - Communicate findings clearly to both technical and non-technical stakeholders, using data storytelling to highlight opportunities, surface risks, and drive alignment on next steps. - Champion data accuracy, consistency, and experimentation best practices to raise the overall bar for measurement and insight generation in Prime Video marketing. Also earlier: "You will design and build scalable analytics solutions, self-service reporting tools, and experimentation frameworks, increasingly powered by AI and automation, that give stakeholders across product, marketing, and science a deep, reliable understanding of customer behavior." Might be duplicate but we can include as part of responsibilities. We need to ensure we don't duplicate lines. We'll combine unique items. Requirements: Basic Qualifications list. Let's extract each bullet: - 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience - 1+ years of SQL, ETL or Oracle experience - 1+ years of processing large, multi-dimensional datasets from multiple sources experience - 1+ years of performing statistical analysis experience - 1+ years of developing automated reporting experience - 3+ years of in the job offered or a related occupation experience (this seems weird; maybe "3+ years of experience in the job offered or a related occupation") - 1+ years of using SQL, ETL (Extract, Transform, Load), or Oracle experience (duplicate of earlier) - Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field - Experience with data visualization using Tableau, Quicksight, or similar tools - Experience with data modeling, warehousing and building ETL pipelines - Experience in Statistical Analysis packages such as R, SAS and Matlab - Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling We need to deduplicate: there are multiple SQL/E/Oracle lines. We'll combine into a concise list but must preserve all substantive info; we can combine similar but not lose detail. However we must not invent or summarize away detail; but we can combine similar items as long as we keep the substance. The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." Combining similar items could be considered summarizing? Might be okay if we keep the same meaning. Safer to list each distinct requirement as separate bullet, even if similar. But we should avoid duplicate lines exactly. We'll keep unique items. Let's list unique: - 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. - 1+ years of SQL, ETL or Oracle experience - 1+ years of processing large, multi-dimensional datasets from multiple sources - 1+ years of performing statistical analysis - 1+ years of developing automated reporting - 3+ years of experience in the job offered or a related occupation - Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field - Experience with data visualization using Tableau, Quicksight, or similar tools - Experience with data modeling, warehousing and building ETL pipelines - Experience in Statistical Analysis packages such as R, SAS and Matlab - Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling Note: "1+ years of using SQL, ETL (Extract, Transform, Load), or Oracle experience" is similar to second; we can drop duplicate. Qualifications: Preferred Qualifications: - Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift - Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets We could also include maybe other preferred but that's it. Skills: Could be derived from tools: SQL, ETL, Redshift, Oracle, NoSQL, Tableau, Quicksight, R, SAS, Matlab, Python, AWS EC2, DynamoDB, S3, Redshift (again). But we might not need a separate Skills section if we already covered in Requirements/Qualifications. However instruction: we can include Skills section if there is real specific content. We have skills mentioned; we could make a Skills section listing technical skills. But we must not create thin sections. Might be okay to have Skills section summarizing tools. However we already have Requirements and Qualifications covering experience; Skills could be redundant. Safer to omit Skills section unless we have distinct skill list not covered elsewhere. The posting mentions "Experience with data visualization using Tableau, Quicksight, or similar tools", "Experience with data modeling, warehousing and building ETL pipelines", "Experience in Statistical Analysis packages such as R, SAS and Matlab", "Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling", "Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift". These are skills. Could be placed under Skills. But we already have Requirements and Qualifications covering experience; maybe we can combine them. However the instruction says "Prefer a few substantive sections over many thin ones." So we could merge skills into Requirements or Qualifications. But we need to follow the headings list: we can include only those headings that have real specific content. If we decide not to have a Skills section, we omit it. Benefits: we have description: "Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave." Also mention sign-on payments and RSUs (these are compensation, not benefits). We'll include Benefits section with that list. Pay: we have base salary range: "USA, WA, SEATTLE - 99,500.00 - 160,000.00 USD annually". Also mention sign-on payments and RSUs. We'll include Pay section with that. Schedule: not present, omit. Now we need to output HTML fragment only. Structure:
About the role
... (the paragraph)
Responsibilities
- ... ...
Requirements
- ... ...
Qualifications
- ... ...
Benefits
- ... ...
Pay
...
Make sure not to include any extra text. Now craft the content precisely. About the role paragraph: combine the two paragraphs into one? Could be twobut we can keep as one
with line breaks? Better to keep as one
but it's long; still okay. Let's write:
Prime Video is changing the way customers watch movies, TV shows, live events, and channels—offering unparalleled choice and convenience across devices such as Fire TV, mobile phones, game consoles, and connected TVs. We deliver world-class viewing experiences to customers in over 240 countries and territories. The Prime Video Product Analytics (PVPA) team is a centralized analytics organization supporting the Prime Video Product organization. We bring together Business Intelligence Engineers, Data Engineers, and Data Scientists to deliver comprehensive data tools and insights that drive product decisions and enhance customer experience. Our work spans the full analytics lifecycle—from data infrastructure and pipeline development to advanced analytics, customer insights modeling, AI-driven solutions, and self-service reporting. This role sits within the PVPA team focused on out-of-app engagement and full-funnel customer journey analytics. Our mission is to understand and optimize how customers discover, engage with, and return to Prime Video