Scaled Insights Analyst, Intelligence and Scaled Insights, YouTube
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Preserve all substantive info, remove boilerplate (cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions). Include a section ONLY when posting has real specific content for it. Avoid filler like "commensurate with experience". Pay/Schedule/Benefits sections only if posting actually states them.
Let's parse the posting.
Content:
Minimum qualifications: Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience. 7 years of experience coding in Python and SQL. 7 years of experience designing, deploying, and managing data pipelines, including schema management, data modeling, exploratory querying, and data workflows.
Preferred qualifications: Experience with data warehousing or data modeling concepts. Experience with big data technologies (e.g., Hadoop, Spark, Python, or R). Experience with translating business requirements to BI solutions. Experience with explaining technical analysis to stakeholders.
About The Job YouTube's Intelligence and Scaled Insights function dissects content risks across social media to support decision-making, ensuring YouTube is a safe place for its billions of users. Resourceful yet technical, this team's work runs the gamut from scaled and strategic to detailed and in-depth. Our Scaled Insights pillar leverages AI to build technology, tools, and scaled capabilities in support of our larger team and the rest of YouTube. Our Intelligence Desk, which Scaled Insights supports, identifies and investigates content risks to ultimately drive mitigations for the benefit of our users. As part of our team, you will be a key contributor toward building an exceptional intelligence function, enjoying a career at the intersection of technology, online safety, and real-world impact. As a scaled insights analyst, you will work with analysts on YouTube Intelligence Desk and other teams across YouTube to translate information needs into tangible technical pathways that ultimately satisfy those information needs. You will leverage Google's suite of AI and technical capabilities to build bespoke capabilities that identify trends and glean difficult to acquire information in support of YouTube's mission, with an emphasis on Trust and Safety and keeping our users safe. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $141000 - $205000 (USD) + 15% bonus target + equity + benefits
Responsibilities Learn more about benefits at Google . Design, build, and optimize robust, production-grade data architecture and ETL pipelines. Serve as the technical steward for the team's data platforms, ensuring maximum data reliability, integrity, and scalable governance across complex datasets. Partner closely with stakeholders to translate ambiguous product and threat-intelligence analytical needs into structured data models, pipelines, and self-serve services. Design, implement, and evaluate intelligent, agentic analytics systems leveraging Large Language Models (LLMs) to automate deep insight generation, anomaly detection, and operational workflows. Architect and maintain internal tools, frameworks, and foundational tables that enable self-serve analytics, reducing data friction and dependencies for non-technical stakeholders. Streamline complex SQL queries and scale value-creating data integrations into automated, repeatable workflows. Own ambiguous, non-routine technical challenges end-to-end. Identify latent infrastructure vulnerabilities, process messy unstructured data volumes, and apply advanced data modeling frameworks with minimal supervision.
Then there is EEO boilerplate etc. Remove.
We need sections: About the role (maybe "About the job" or "About The Job"? The instruction says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can use "About the role". The posting has "About The Job". We'll map to "About the role". Provide prose.
Responsibilities: list of duties.
Requirements: maybe "Minimum qualifications" and "Preferred qualifications". Could combine into "Requirements" or separate "Qualifications". The instruction includes "Qualifications" as a possible heading. We can have "Qualifications" with sub-sections? But we need to use - for any list of duties/requirements/benefits. So we can list minimum qualifications as
- items. Preferred qualifications also as list.
Skills: maybe we can extract skills from qualifications: Python, SQL, data pipelines, schema management, data modeling, exploratory querying, data workflows, data warehousing, big data technologies (Hadoop, Spark, Python, R), translating business requirements to BI solutions, explaining technical analysis to stakeholders. Could be a Skills section. But we already have qualifications; maybe we can combine. The instruction says prefer a few substantive sections over many thin ones. So we could have sections: About the role, Responsibilities, Qualifications (including minimum and preferred), Pay and Benefits (if present). Pay is present: US: $141000 - $205000 (USD) + 15% bonus target + equity + benefits. Benefits: mention "benefits" but not detailed. We have "benefits" mentioned generically. Should we create a Benefits section? The posting says "+ equity + benefits". It does not list specific benefits. The instruction: never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state benefits generically, but not specific. Might be okay to include a Benefits section with generic statement? However they said avoid filler like "not specified", "N/A", "varies", "TBD". "benefits" is vague but it's stated. Could include a short
that says "Benefits include equity and other standard Google benefits." But that might be adding detail not present. Safer to not create Benefits section if no specific benefits listed. However the posting says "+ equity + benefits". That is a mention of benefits but not details. Might be considered substantive enough? The instruction: "never create Pay/Schedule/Benefits sections unless the posting actually states them." It does state benefits (the word). So we could include a Benefits section with a short
that says "Benefits include equity and other standard Google benefits." But that is inferring. Might be considered adding detail. Safer to include a Benefits section with a
that says "Benefits include equity and other benefits as part of Google's total compensation package." That's still inference. Maybe better to include Pay section only, and mention benefits within Pay section? The instruction says use
for Benefits if present. Could combine Pay and Benefits into one section? Not listed but we could have Pay section that includes both pay and benefits. However they gave separate headings examples. We could create a Pay section with the pay details and mention benefits briefly. That seems acceptable.
Schedule: not mentioned, so omit.
Thus sections: About the role, Responsibilities, Qualifications, Pay.
Now we need to format.
About the role
... prose from About The Job ...
We need to preserve all substantive information from that paragraph. Let's extract the prose:
"YouTube's Intelligence and Scaled Insights function dissects content risks across social media to support decision-making, ensuring YouTube is a safe place for its billions of users. Resourceful yet technical, this team's work runs the gamut from scaled and strategic to detailed and in-depth. Our Scaled Insights pillar leverages AI to build technology, tools, and scaled capabilities in support of our larger team and the rest of YouTube. Our Intelligence Desk, which Scaled Insights supports, identifies and investigates content risks to ultimately drive mitigations for the benefit of our users. As part of our team, you will be a key contributor toward building an exceptional intelligence function, enjoying a career at the intersection of technology, online safety, and real-world impact. As a scaled insights analyst, you will work with analysts on YouTube Intelligence Desk and other teams across YouTube to translate information needs into tangible technical pathways that ultimately satisfy those information needs. You will leverage Google's suite of AI and technical capabilities to build bespoke capabilities that identify trends and glean difficult to acquire information in support of YouTube's mission, with an emphasis on Trust and Safety and keeping our users safe. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together."
We need to keep that as . Possibly break into multiple
but okay.
Responsibilities: list items.
From responsibilities paragraph: "Design, build, and optimize robust, production-grade data architecture and ETL pipelines. Serve as the technical steward for the team's data platforms, ensuring maximum data reliability, integrity, and scalable governance across complex datasets. Partner closely with stakeholders to translate ambiguous product and threat-intelligence analytical needs into structured data models, pipelines, and self-serve services. Design, implement, and evaluate intelligent, agentic analytics systems leveraging Large Language Models (LLMs) to automate deep insight generation, anomaly detection, and operational workflows. Architect and maintain internal tools, frameworks, and foundational tables that enable self-serve analytics, reducing data friction and dependencies for non-technical stakeholders. Streamline complex SQL queries and scale value-creating data integrations into automated, repeatable workflows. Own ambiguous, non-routine technical challenges end-to-end. Identify latent infrastructure vulnerabilities, process messy unstructured data volumes, and apply advanced data modeling frameworks with minimal supervision."
We'll make each sentence a
- .
Qualifications: Minimum and Preferred.
Minimum qualifications list:
- Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
- 7 years of experience coding in Python and SQL.
- 7 years of experience designing, deploying, and managing data pipelines, including schema management, data modeling, exploratory querying, and data workflows.
Preferred qualifications list:
- Experience with data warehousing or data modeling concepts.
- Experience with big data technologies (e.g., Hadoop, Spark, Python, or R).
- Experience with translating business requirements to BI solutions.
- Experience with explaining technical analysis to stakeholders.
We'll put under
Qualifications
maybe with two subheadings? But we cannot add extra headings beyond those listed? The instruction only says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid additional headings, but better to stick to those. Could we have two under Qualifications, one for Minimum, one for Preferred, with preceding indicating? That might be okay. Or we could combine all into one list but differentiate. Better to separate with
Minimum qualifications: then
. Then Preferred qualifications: then
. That's fine.
Pay section: Pay
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $141,000
for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Preserve all substantive info, remove boilerplate (cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions). Include a section ONLY when posting has real specific content for it. Avoid filler like "commensurate with experience". Pay/Schedule/Benefits sections only if posting actually states them. Let's parse the posting. Content: Minimum qualifications: Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience. 7 years of experience coding in Python and SQL. 7 years of experience designing, deploying, and managing data pipelines, including schema management, data modeling, exploratory querying, and data workflows. Preferred qualifications: Experience with data warehousing or data modeling concepts. Experience with big data technologies (e.g., Hadoop, Spark, Python, or R). Experience with translating business requirements to BI solutions. Experience with explaining technical analysis to stakeholders. About The Job YouTube's Intelligence and Scaled Insights function dissects content risks across social media to support decision-making, ensuring YouTube is a safe place for its billions of users. Resourceful yet technical, this team's work runs the gamut from scaled and strategic to detailed and in-depth. Our Scaled Insights pillar leverages AI to build technology, tools, and scaled capabilities in support of our larger team and the rest of YouTube. Our Intelligence Desk, which Scaled Insights supports, identifies and investigates content risks to ultimately drive mitigations for the benefit of our users. As part of our team, you will be a key contributor toward building an exceptional intelligence function, enjoying a career at the intersection of technology, online safety, and real-world impact. As a scaled insights analyst, you will work with analysts on YouTube Intelligence Desk and other teams across YouTube to translate information needs into tangible technical pathways that ultimately satisfy those information needs. You will leverage Google's suite of AI and technical capabilities to build bespoke capabilities that identify trends and glean difficult to acquire information in support of YouTube's mission, with an emphasis on Trust and Safety and keeping our users safe. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $141000 - $205000 (USD) + 15% bonus target + equity + benefits Responsibilities Learn more about benefits at Google . Design, build, and optimize robust, production-grade data architecture and ETL pipelines. Serve as the technical steward for the team's data platforms, ensuring maximum data reliability, integrity, and scalable governance across complex datasets. Partner closely with stakeholders to translate ambiguous product and threat-intelligence analytical needs into structured data models, pipelines, and self-serve services. Design, implement, and evaluate intelligent, agentic analytics systems leveraging Large Language Models (LLMs) to automate deep insight generation, anomaly detection, and operational workflows. Architect and maintain internal tools, frameworks, and foundational tables that enable self-serve analytics, reducing data friction and dependencies for non-technical stakeholders. Streamline complex SQL queries and scale value-creating data integrations into automated, repeatable workflows. Own ambiguous, non-routine technical challenges end-to-end. Identify latent infrastructure vulnerabilities, process messy unstructured data volumes, and apply advanced data modeling frameworks with minimal supervision. Then there is EEO boilerplate etc. Remove. We need sections: About the role (maybe "About the job" or "About The Job"? The instruction says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can use "About the role". The posting has "About The Job". We'll map to "About the role". Provide prose.
Responsibilities: list of duties.
Requirements: maybe "Minimum qualifications" and "Preferred qualifications". Could combine into "Requirements" or separate "Qualifications". The instruction includes "Qualifications" as a possible heading. We can have "Qualifications" with sub-sections? But we need to use - for any list of duties/requirements/benefits. So we can list minimum qualifications as
- items. Preferred qualifications also as list.
Skills: maybe we can extract skills from qualifications: Python, SQL, data pipelines, schema management, data modeling, exploratory querying, data workflows, data warehousing, big data technologies (Hadoop, Spark, Python, R), translating business requirements to BI solutions, explaining technical analysis to stakeholders. Could be a Skills section. But we already have qualifications; maybe we can combine. The instruction says prefer a few substantive sections over many thin ones. So we could have sections: About the role, Responsibilities, Qualifications (including minimum and preferred), Pay and Benefits (if present). Pay is present: US: $141000 - $205000 (USD) + 15% bonus target + equity + benefits. Benefits: mention "benefits" but not detailed. We have "benefits" mentioned generically. Should we create a Benefits section? The posting says "+ equity + benefits". It does not list specific benefits. The instruction: never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state benefits generically, but not specific. Might be okay to include a Benefits section with generic statement? However they said avoid filler like "not specified", "N/A", "varies", "TBD". "benefits" is vague but it's stated. Could include a short
that says "Benefits include equity and other standard Google benefits." But that might be adding detail not present. Safer to not create Benefits section if no specific benefits listed. However the posting says "+ equity + benefits". That is a mention of benefits but not details. Might be considered substantive enough? The instruction: "never create Pay/Schedule/Benefits sections unless the posting actually states them." It does state benefits (the word). So we could include a Benefits section with a short
that says "Benefits include equity and other standard Google benefits." But that is inferring. Might be considered adding detail. Safer to include a Benefits section with a
that says "Benefits include equity and other benefits as part of Google's total compensation package." That's still inference. Maybe better to include Pay section only, and mention benefits within Pay section? The instruction says use
for Benefits if present. Could combine Pay and Benefits into one section? Not listed but we could have Pay section that includes both pay and benefits. However they gave separate headings examples. We could create a Pay section with the pay details and mention benefits briefly. That seems acceptable.
Schedule: not mentioned, so omit.
Thus sections: About the role, Responsibilities, Qualifications, Pay.
Now we need to format.
About the role
... prose from About The Job ...
We need to preserve all substantive information from that paragraph. Let's extract the prose:
"YouTube's Intelligence and Scaled Insights function dissects content risks across social media to support decision-making, ensuring YouTube is a safe place for its billions of users. Resourceful yet technical, this team's work runs the gamut from scaled and strategic to detailed and in-depth. Our Scaled Insights pillar leverages AI to build technology, tools, and scaled capabilities in support of our larger team and the rest of YouTube. Our Intelligence Desk, which Scaled Insights supports, identifies and investigates content risks to ultimately drive mitigations for the benefit of our users. As part of our team, you will be a key contributor toward building an exceptional intelligence function, enjoying a career at the intersection of technology, online safety, and real-world impact. As a scaled insights analyst, you will work with analysts on YouTube Intelligence Desk and other teams across YouTube to translate information needs into tangible technical pathways that ultimately satisfy those information needs. You will leverage Google's suite of AI and technical capabilities to build bespoke capabilities that identify trends and glean difficult to acquire information in support of YouTube's mission, with an emphasis on Trust and Safety and keeping our users safe. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together."
We need to keep that as . Possibly break into multiple
but okay.
Responsibilities: list items.
From responsibilities paragraph: "Design, build, and optimize robust, production-grade data architecture and ETL pipelines. Serve as the technical steward for the team's data platforms, ensuring maximum data reliability, integrity, and scalable governance across complex datasets. Partner closely with stakeholders to translate ambiguous product and threat-intelligence analytical needs into structured data models, pipelines, and self-serve services. Design, implement, and evaluate intelligent, agentic analytics systems leveraging Large Language Models (LLMs) to automate deep insight generation, anomaly detection, and operational workflows. Architect and maintain internal tools, frameworks, and foundational tables that enable self-serve analytics, reducing data friction and dependencies for non-technical stakeholders. Streamline complex SQL queries and scale value-creating data integrations into automated, repeatable workflows. Own ambiguous, non-routine technical challenges end-to-end. Identify latent infrastructure vulnerabilities, process messy unstructured data volumes, and apply advanced data modeling frameworks with minimal supervision."
We'll make each sentence a
- .
Qualifications: Minimum and Preferred.
Minimum qualifications list:
- Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
- 7 years of experience coding in Python and SQL.
- 7 years of experience designing, deploying, and managing data pipelines, including schema management, data modeling, exploratory querying, and data workflows.
Preferred qualifications list:
- Experience with data warehousing or data modeling concepts.
- Experience with big data technologies (e.g., Hadoop, Spark, Python, or R).
- Experience with translating business requirements to BI solutions.
- Experience with explaining technical analysis to stakeholders.
We'll put under
Qualifications
maybe with two subheadings? But we cannot add extra headings beyond those listed? The instruction only says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid additional headings, but better to stick to those. Could we have two under Qualifications, one for Minimum, one for Preferred, with preceding indicating? That might be okay. Or we could combine all into one list but differentiate. Better to separate with
Minimum qualifications: then
. Then Preferred qualifications: then
. That's fine.
Pay section: Pay
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $141,000
- items. Preferred qualifications also as list.
Skills: maybe we can extract skills from qualifications: Python, SQL, data pipelines, schema management, data modeling, exploratory querying, data workflows, data warehousing, big data technologies (Hadoop, Spark, Python, R), translating business requirements to BI solutions, explaining technical analysis to stakeholders. Could be a Skills section. But we already have qualifications; maybe we can combine. The instruction says prefer a few substantive sections over many thin ones. So we could have sections: About the role, Responsibilities, Qualifications (including minimum and preferred), Pay and Benefits (if present). Pay is present: US: $141000 - $205000 (USD) + 15% bonus target + equity + benefits. Benefits: mention "benefits" but not detailed. We have "benefits" mentioned generically. Should we create a Benefits section? The posting says "+ equity + benefits". It does not list specific benefits. The instruction: never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state benefits generically, but not specific. Might be okay to include a Benefits section with generic statement? However they said avoid filler like "not specified", "N/A", "varies", "TBD". "benefits" is vague but it's stated. Could include a short
that says "Benefits include equity and other standard Google benefits." But that might be adding detail not present. Safer to not create Benefits section if no specific benefits listed. However the posting says "+ equity + benefits". That is a mention of benefits but not details. Might be considered substantive enough? The instruction: "never create Pay/Schedule/Benefits sections unless the posting actually states them." It does state benefits (the word). So we could include a Benefits section with a short
that says "Benefits include equity and other standard Google benefits." But that is inferring. Might be considered adding detail. Safer to include a Benefits section with a
that says "Benefits include equity and other benefits as part of Google's total compensation package." That's still inference. Maybe better to include Pay section only, and mention benefits within Pay section? The instruction says use
for Benefits if present. Could combine Pay and Benefits into one section? Not listed but we could have Pay section that includes both pay and benefits. However they gave separate headings examples. We could create a Pay section with the pay details and mention benefits briefly. That seems acceptable. Schedule: not mentioned, so omit. Thus sections: About the role, Responsibilities, Qualifications, Pay. Now we need to format.
About the role
... prose from About The Job ...
We need to preserve all substantive information from that paragraph. Let's extract the prose: "YouTube's Intelligence and Scaled Insights function dissects content risks across social media to support decision-making, ensuring YouTube is a safe place for its billions of users. Resourceful yet technical, this team's work runs the gamut from scaled and strategic to detailed and in-depth. Our Scaled Insights pillar leverages AI to build technology, tools, and scaled capabilities in support of our larger team and the rest of YouTube. Our Intelligence Desk, which Scaled Insights supports, identifies and investigates content risks to ultimately drive mitigations for the benefit of our users. As part of our team, you will be a key contributor toward building an exceptional intelligence function, enjoying a career at the intersection of technology, online safety, and real-world impact. As a scaled insights analyst, you will work with analysts on YouTube Intelligence Desk and other teams across YouTube to translate information needs into tangible technical pathways that ultimately satisfy those information needs. You will leverage Google's suite of AI and technical capabilities to build bespoke capabilities that identify trends and glean difficult to acquire information in support of YouTube's mission, with an emphasis on Trust and Safety and keeping our users safe. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together." We need to keep that as. Possibly break into multiple
but okay. Responsibilities: list items. From responsibilities paragraph: "Design, build, and optimize robust, production-grade data architecture and ETL pipelines. Serve as the technical steward for the team's data platforms, ensuring maximum data reliability, integrity, and scalable governance across complex datasets. Partner closely with stakeholders to translate ambiguous product and threat-intelligence analytical needs into structured data models, pipelines, and self-serve services. Design, implement, and evaluate intelligent, agentic analytics systems leveraging Large Language Models (LLMs) to automate deep insight generation, anomaly detection, and operational workflows. Architect and maintain internal tools, frameworks, and foundational tables that enable self-serve analytics, reducing data friction and dependencies for non-technical stakeholders. Streamline complex SQL queries and scale value-creating data integrations into automated, repeatable workflows. Own ambiguous, non-routine technical challenges end-to-end. Identify latent infrastructure vulnerabilities, process messy unstructured data volumes, and apply advanced data modeling frameworks with minimal supervision." We'll make each sentence a
- .
Qualifications: Minimum and Preferred.
Minimum qualifications list:
- Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
- 7 years of experience coding in Python and SQL.
- 7 years of experience designing, deploying, and managing data pipelines, including schema management, data modeling, exploratory querying, and data workflows.
Preferred qualifications list:
- Experience with data warehousing or data modeling concepts.
- Experience with big data technologies (e.g., Hadoop, Spark, Python, or R).
- Experience with translating business requirements to BI solutions.
- Experience with explaining technical analysis to stakeholders.
We'll put under
Qualifications
maybe with two subheadings? But we cannot add extra headings beyond those listed? The instruction only says usefor section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid additional headings, but better to stick to those. Could we have two
- under Qualifications, one for Minimum, one for Preferred, with preceding
indicating? That might be okay. Or we could combine all into one list but differentiate. Better to separate with
Minimum qualifications: then
- . Then
Preferred qualifications: then
- . That's fine.
Pay section:
Pay
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $141,000