Lead Data Architect
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So we should map content to these standard headings. We need to include sections only when posting has real specific content.
We have:
- About the role: could be company overview + what you'll do summary. Use About the role
with paragraphs.
- Responsibilities: list from "Responsibility" bullet points (the paragraph after "Responsibility").
- Requirements/Qualifications: from "What you bring" (basic and preferred?). Could separate into Requirements (basic) and Preferred (preferred). But we can combine into Requirements section with both basic and preferred? The instruction: prefer few substantive sections over many thin ones. Could have Requirements (basic) and maybe Skills? But we have "Preferred" separate. Could treat "Preferred" as part of Requirements or as Skills? Might be better to have Requirements (basic) and Preferred (maybe as "Preferred Qualifications").
But we need to follow headings list: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule.
We have Pay info: Wage Transparency with ranges for CA and WA, plus bonus info, RSU. So we can create Pay section.
Benefits: we have Benefits bullet list.
Schedule: we have Job Designation Hybrid info (minimum 2 days per week). That's schedule.
Thus we can output sections: About the role, Responsibilities, Requirements, Benefits, Pay, Schedule.
We might also include Qualifications? Could merge Requirements and Qualifications but we have both basic and preferred; maybe Requirements for basic, Qualifications for preferred? But we can keep simple: Requirements (basic) and Preferred as maybe Skills? However "Skills" heading could be used for technical skills list. The "What you bring" includes many technical skills; we could put those under Skills. But we also have basic education and experience under Requirements. Let's decide:
- About the role: company overview + what you'll do.
- Responsibilities: list of duties.
- Requirements: basic qualifications (Bachelor's, Master's, 12+ years experience, etc.)
- Skills: technical skills list (database structure, SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, Airflow, dbt, Snowflake, Aurora, CRM systems, marketing data, attribution models, etc., data security, ML/AI, project management).
- Benefits: bullet list.
- Pay: wage transparency + bonus + RSU.
- Schedule: hybrid designation.
We must not include headings with filler text. Ensure we have substantive content under each.
We must not repeat job title as heading. Not include Job ID etc.
We must remove boilerplate: Work Authorization Notice, Accommodation, Applicant and Candidate Privacy Notice, States Not Eligible for Employment, Equal Opportunity Employer, EEO Know Your Rights poster, Life at Docusign maybe? It's not boilerplate but could be considered culture; but we can omit to keep sections minimal. The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." Life at Docusign is not legal boilerplate; but it's more fluff. Could keep as part of About the role? Might be okay to omit. Safer to keep only substantive sections.
Now craft HTML.
We need to use for each section heading. Use - for lists. Use
for prose.
Let's extract content.
Company Overview paragraph: "Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM)."
What you'll do: "We are looking for a skilled and experienced Marketing Data Architect to join our Enterprise Data Warehousing Team. The successful candidate will play a critical role in designing, developing, and maintaining the data architecture specifically for marketing analytics and insights. This role focuses on creating a robust infrastructure that supports large-scale data collection, storage, and analysis, helping marketing teams make informed decisions. This is an exciting opportunity to be part of a driven team and continue to build experience and expertise by solving modern business problems and having exposure to cutting edge technologies powering Docusign. We are looking for someone who loves to solve important business problems, has a curiosity for technology, and isn't afraid to challenge the status quo. This position is an individual contributor role reporting to the Sr. Manager, Data & Analytics."
We can combine into About the role.
Responsibilities: list after "Responsibility". The text:
"Design and develop data models and database architecture specifically tailored for marketing data analytics and reporting within a data warehousing environment
Work closely with marketing teams to understand their data needs and translate business requirements into technical specifications
Implement and maintain scalable and efficient data warehouses and data marts that support various marketing functions, including campaign management, customer segmentation, and ROI analysis
Optimize data flows and architecture to improve data accuracy, accessibility, and quality, ensuring data is available for real-time and batch processing
Develop and enforce best practices and standards for data modeling, data extraction, and data loading into the data warehouse
Collaborate with IT and business stakeholders to ensure that the data architecture supports the goals of the marketing team while aligning with the overall business strategy
Prepare and maintain documentation of data architectures, configurations, and processes
Stay up-to-date with industry trends and advancements in data warehousing and marketing technologies to continuously improve the data infrastructure"
Each line as
- .
Requirements (basic): from "What you bring" basic bullet list:
"Basic Bachelor's degree in Computer Science, Information Systems, or a related field
Master's degree
Experience with database structure principles, data modeling, and metadata management
12+ years of proven experience as a Data Architect, Data Engineer, or similar role with a focus on data warehousing and marketing data analytics
Experience designing data platforms for large-scale data and diverse data architectures, including warehouses, lake houses, and integrated data stores
Experience in marketing data and analytics, including attribution models, propensity models, segmentation, contact & customer databases, and campaign tracking
Experience with CRM systems (e.g., Salesforce, Iterable, Eloqua) and their integration with data warehouses
Experience with a variety of technologies: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, data stacks like Airflow, dbt, and data stores like Snowflake, Aurora
Experience with data security (including PHI and PII), as well as data privacy regulations (CCPA and GDPR)
Experience addressing data-related challenges through analytical problem-solving and aligning data architecture with organizational business goals and objectives
Experience with analytics techniques using ML and AI to assist data scientists and analysts in deriving insights from data
Experience managing projects effectively, planning tasks, setting priorities, and meeting deadlines in a fast-paced environment"
We could split: Requirements (education + experience) and Skills (technologies). But many of these are technical skills. Let's treat Requirements as education + years experience + general experience (like data architecture, marketing data, CRM, data security, ML/AI, project management). Skills as specific tech stack: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, Airflow, dbt, Snowflake, Aurora, etc. Also maybe include CRM systems.
But we need to avoid duplication. Let's parse:
Basic bullets:
- Basic Bachelor's degree in Computer Science, Information Systems, or a related field
- Master's degree
- Experience with database structure principles, data modeling, and metadata management
- 12+ years of proven experience as a Data Architect, Data Engineer, or similar role with a focus on data warehousing and marketing data analytics
- Experience designing data platforms for large-scale data and diverse data architectures, including warehouses, lake houses, and integrated data stores
- Experience in marketing data and analytics, including attribution models, propensity models, segmentation, contact & customer databases, and campaign tracking
- Experience with CRM systems (e.g., Salesforce, Iterable, Eloqua) and their integration with data warehouses
- Experience with a variety of technologies: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, data stacks like Airflow, dbt, and data stores like Snowflake, Aurora
- Experience with data security (including PHI and PII), as well as data privacy regulations (CCPA and GDPR)
- Experience addressing data-related challenges through analytical problem-solving and aligning data architecture with organizational business goals and objectives
- Experience with analytics techniques using ML and AI to assist data scientists and analysts in deriving insights from data
- Experience managing projects effectively, planning tasks, setting priorities, and meeting deadlines in a fast-paced environment
We could separate:
Requirements (education + experience): Bachelor's, Master's, 12+ years experience, experience with database structure principles, data modeling, metadata management, experience designing data platforms, experience in marketing data and analytics, experience with CRM systems, experience addressing data-related challenges through analytical problem-solving, experience with ML/AI, experience managing projects.
Skills: technical stack: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, Airflow, dbt, Snowflake, Aurora, data security (PHI/PII, CCPA/GDPR) maybe also skill.
But data security could be both.
Simplify: Have Requirements (basic) list includes education, years experience, and general experience (database structure, data modeling, metadata management, designing data platforms, marketing data analytics, CRM integration, data security/privacy, ML/AI, problem-solving, project management). Skills list includes specific technologies: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, Airflow, dbt, Snowflake, Aurora, plus maybe CRM systems (Salesforce, Iterable, Eloqua). That seems reasonable.
Now Benefits: list from "Benefits":
"Global benefits provide options for the following:
Paid Time Off: earned time off, as well as paid company holidays based on region
Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement
Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment
Retirement Plans: select retirement and pension programs with potential for employer contributions
Learning and Development: options for coaching, online courses and education reimbursements
Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events"
We'll make
- each.
Pay: from
- for lists. Use
for prose. Let's extract content. Company Overview paragraph: "Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM)." What you'll do: "We are looking for a skilled and experienced Marketing Data Architect to join our Enterprise Data Warehousing Team. The successful candidate will play a critical role in designing, developing, and maintaining the data architecture specifically for marketing analytics and insights. This role focuses on creating a robust infrastructure that supports large-scale data collection, storage, and analysis, helping marketing teams make informed decisions. This is an exciting opportunity to be part of a driven team and continue to build experience and expertise by solving modern business problems and having exposure to cutting edge technologies powering Docusign. We are looking for someone who loves to solve important business problems, has a curiosity for technology, and isn't afraid to challenge the status quo. This position is an individual contributor role reporting to the Sr. Manager, Data & Analytics." We can combine into About the role. Responsibilities: list after "Responsibility". The text: "Design and develop data models and database architecture specifically tailored for marketing data analytics and reporting within a data warehousing environment Work closely with marketing teams to understand their data needs and translate business requirements into technical specifications Implement and maintain scalable and efficient data warehouses and data marts that support various marketing functions, including campaign management, customer segmentation, and ROI analysis Optimize data flows and architecture to improve data accuracy, accessibility, and quality, ensuring data is available for real-time and batch processing Develop and enforce best practices and standards for data modeling, data extraction, and data loading into the data warehouse Collaborate with IT and business stakeholders to ensure that the data architecture supports the goals of the marketing team while aligning with the overall business strategy Prepare and maintain documentation of data architectures, configurations, and processes Stay up-to-date with industry trends and advancements in data warehousing and marketing technologies to continuously improve the data infrastructure" Each line as
- .
Requirements (basic): from "What you bring" basic bullet list:
"Basic Bachelor's degree in Computer Science, Information Systems, or a related field
Master's degree
Experience with database structure principles, data modeling, and metadata management
12+ years of proven experience as a Data Architect, Data Engineer, or similar role with a focus on data warehousing and marketing data analytics
Experience designing data platforms for large-scale data and diverse data architectures, including warehouses, lake houses, and integrated data stores
Experience in marketing data and analytics, including attribution models, propensity models, segmentation, contact & customer databases, and campaign tracking
Experience with CRM systems (e.g., Salesforce, Iterable, Eloqua) and their integration with data warehouses
Experience with a variety of technologies: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, data stacks like Airflow, dbt, and data stores like Snowflake, Aurora
Experience with data security (including PHI and PII), as well as data privacy regulations (CCPA and GDPR)
Experience addressing data-related challenges through analytical problem-solving and aligning data architecture with organizational business goals and objectives
Experience with analytics techniques using ML and AI to assist data scientists and analysts in deriving insights from data
Experience managing projects effectively, planning tasks, setting priorities, and meeting deadlines in a fast-paced environment"
We could split: Requirements (education + experience) and Skills (technologies). But many of these are technical skills. Let's treat Requirements as education + years experience + general experience (like data architecture, marketing data, CRM, data security, ML/AI, project management). Skills as specific tech stack: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, Airflow, dbt, Snowflake, Aurora, etc. Also maybe include CRM systems.
But we need to avoid duplication. Let's parse:
Basic bullets:
- Basic Bachelor's degree in Computer Science, Information Systems, or a related field
- Master's degree
- Experience with database structure principles, data modeling, and metadata management
- 12+ years of proven experience as a Data Architect, Data Engineer, or similar role with a focus on data warehousing and marketing data analytics
- Experience designing data platforms for large-scale data and diverse data architectures, including warehouses, lake houses, and integrated data stores
- Experience in marketing data and analytics, including attribution models, propensity models, segmentation, contact & customer databases, and campaign tracking
- Experience with CRM systems (e.g., Salesforce, Iterable, Eloqua) and their integration with data warehouses
- Experience with a variety of technologies: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, data stacks like Airflow, dbt, and data stores like Snowflake, Aurora
- Experience with data security (including PHI and PII), as well as data privacy regulations (CCPA and GDPR)
- Experience addressing data-related challenges through analytical problem-solving and aligning data architecture with organizational business goals and objectives
- Experience with analytics techniques using ML and AI to assist data scientists and analysts in deriving insights from data
- Experience managing projects effectively, planning tasks, setting priorities, and meeting deadlines in a fast-paced environment
We could separate:
Requirements (education + experience): Bachelor's, Master's, 12+ years experience, experience with database structure principles, data modeling, metadata management, experience designing data platforms, experience in marketing data and analytics, experience with CRM systems, experience addressing data-related challenges through analytical problem-solving, experience with ML/AI, experience managing projects.
Skills: technical stack: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, Airflow, dbt, Snowflake, Aurora, data security (PHI/PII, CCPA/GDPR) maybe also skill.
But data security could be both.
Simplify: Have Requirements (basic) list includes education, years experience, and general experience (database structure, data modeling, metadata management, designing data platforms, marketing data analytics, CRM integration, data security/privacy, ML/AI, problem-solving, project management). Skills list includes specific technologies: SQL, Bash, Python, Java, Spark, AWS, DataOps, DevSecOps, Airflow, dbt, Snowflake, Aurora, plus maybe CRM systems (Salesforce, Iterable, Eloqua). That seems reasonable.
Now Benefits: list from "Benefits":
"Global benefits provide options for the following:
Paid Time Off: earned time off, as well as paid company holidays based on region
Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement
Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment
Retirement Plans: select retirement and pension programs with potential for employer contributions
Learning and Development: options for coaching, online courses and education reimbursements
Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events"
We'll make
- each. Pay: from