Assistant Vice President - Data Architect
EXL · New York, United States · 2 wk ago
EngineeringFull-time
We need to output HTML fragment with sections only when content present. Use for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content.
We have job description with sections: Role Overview (we can treat as About the role). Key Responsibilities -> Responsibilities. Required Technical Skills -> Requirements? Or Skills? The instruction: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which content fits which heading.
We have: Role Overview (About the role). Key Responsibilities (Responsibilities). Required Technical Skills (maybe Requirements or Skills). There's also Preferred Skills, Soft Skills, Educational Qualifications, Preferred Certifications, Key Competencies, Ideal Candidate Profile.
We need to avoid filler sections. We should include sections only if we have substantive content.
We can combine some: Requirements could be Required Technical Skills + Preferred Skills? But we need to not invent content. We can have separate sections: Requirements (maybe Required Technical Skills), Skills (maybe Preferred Skills + Soft Skills?), Qualifications (Educational Qualifications + Certifications). Benefits, Pay, Schedule: none present, so omit those sections.
We must not repeat job title as heading. No Job ID.
We must preserve all substantive information, not invent or summarize away detail. So we need to keep details like bullet points.
We need to use
for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content.
We have job description with sections: Role Overview (we can treat as About the role). Key Responsibilities -> Responsibilities. Required Technical Skills -> Requirements? Or Skills? The instruction: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which content fits which heading.
We have: Role Overview (About the role). Key Responsibilities (Responsibilities). Required Technical Skills (maybe Requirements or Skills). There's also Preferred Skills, Soft Skills, Educational Qualifications, Preferred Certifications, Key Competencies, Ideal Candidate Profile.
We need to avoid filler sections. We should include sections only if we have substantive content.
We can combine some: Requirements could be Required Technical Skills + Preferred Skills? But we need to not invent content. We can have separate sections: Requirements (maybe Required Technical Skills), Skills (maybe Preferred Skills + Soft Skills?), Qualifications (Educational Qualifications + Certifications). Benefits, Pay, Schedule: none present, so omit those sections.
We must not repeat job title as heading. No Job ID.
We must preserve all substantive information, not invent or summarize away detail. So we need to keep details like bullet points.
We need to use - for any list of duties/requirements/benefits, and
for prose.
Thus for each section we need to output
Heading
then either for prose or
- for list.
We need to decide how to structure lists.
Let's extract content:
About the role (Role Overview):
"We are seeking an experienced Data Architect with 12-15 years of experience in designing and implementing enterprise-scale data platforms, data architectures, and data management solutions. The ideal candidate will possess strong expertise in data modeling, data warehousing, big data ecosystems, cloud data platforms, and modern data architecture frameworks. The Data Architect will be responsible for defining the organization's data strategy, designing scalable and secure data solutions, establishing best practices, and providing architectural leadership to data engineering, analytics, and business teams."
That's prose.
Responsibilities (Key Responsibilities):
List bullet points:
- Define and implement enterprise data architecture strategy aligned with business objectives.
- Design scalable, secure, and high-performing data platforms supporting analytics, reporting, AI/ML, and operational workloads.
- Develop conceptual, logical, and physical data models for enterprise applications.
- Architect enterprise data warehouses, data lakes, lakehouses, and modern data platforms.
- Establish data governance, metadata management, data quality, and master data management frameworks.
- Lead the design of data integration and ETL/ELT architectures across multiple systems.
- Define standards, best practices, and architectural guidelines for data management.
- Collaborate with business leaders, enterprise architects, and technology teams to drive data transformation initiatives.
- Evaluate emerging technologies and recommend innovative data solutions.
- Provide technical leadership and mentorship to data engineering and analytics teams.
- Ensure compliance with security, privacy, and regulatory requirements.
Requirements (Required Technical Skills):
We have many sub-bullets. Could structure as nested lists? Probably just flat list of items. But we need to preserve all detail. Could break into categories as subheadings? But only allowed headings are the predefined ones; we cannot add extra headings. So we need to put everything under Requirements as a list, maybe with sub-bullets using
- and within li we can include prose describing subcategories. That's acceptable.
We need to include all bullet points under Required Technical Skills.
Let's extract them:
Data Architecture & Design - Strong expertise in:
- Enterprise Data Architecture
- Data Strategy & Roadmaps
- Data Integration Architecture
- Information Architecture
- Reference Architecture Development
- Data Modernization Programs
Data Modeling - Extensive experience with:
- Conceptual Data Modeling
- Logical Data Modeling
- Physical Data Modeling
- Dimensional Modeling
- Data Vault 2.0
- Star & Snowflake Schemas
- Proficiency with data modeling tools such as:
- Erwin
- ER/Studio
- PowerDesigner
- Visio
Data Warehousing & Data Lakes - Strong experience designing:
- Enterprise Data Warehouses
- Data Lakes
- Lakehouse Architectures
- Operational Data Stores (ODS)
- Data Marts
- Deep understanding of:
- Data Lifecycle Management
- Metadata Management
- Data Lineage
Cloud & Modern Data Platforms - Strong experience in one or more cloud platforms:
Microsoft Azure:
- Azure Data Factory
- Azure Synapse Analytics
- Azure Data Lake Storage
- Microsoft Fabric
- Azure Databricks
Amazon Web Services (AWS):
- S3
- Glue
- Redshift
- EMR
- Athena
Google Cloud Platform (GCP):
- BigQuery
- Dataflow
- Dataproc
- Cloud Storage
Big Data & Data Engineering - Strong understanding of:
- Hadoop Ecosystem
- Spark / PySpark
- Hive
- Kafka
- Delta Lake
- Experience designing large-scale batch and real-time data processing architectures.
Database Technologies Experience Working With:
- SQL Server
- Oracle
- PostgreSQL
- Snowflake
- Teradata
- MySQL
- Strong Expertise In Database Design Query Optimization Data Partitioning Performance Tuning
Data Governance & Security - Strong knowledge of:
- Data Governance Frameworks
- Metadata Management
- Data Cataloging
- Data Quality Management
- Master Data Management (MDM)
- Data Privacy Regulations
- Experience implementing:
- Role-Based Access Control (RBAC)
- Data Security Policies
- Data Compliance Frameworks
Architecture & Integration Skills - Experience with:
- Enterprise Integration Patterns
- API-Based Data Integration
- Event-Driven Architecture
- Microservices Architecture
- Data Mesh Concepts
- Data Fabric Architecture
- Ability to create architecture artifacts, solution blueprints, and data flow diagrams.
Leadership Responsibilities (maybe part of Responsibilities? but we already have Responsibilities section; we could include leadership responsibilities there as part of responsibilities list. However the JD includes a separate "Leadership Responsibilities" bullet list after Required Technical Skills. We could incorporate those into Responsibilities as additional items. That would preserve all info.
Preferred Skills:
- Experience with:
- Snowflake
- Databricks
- Microsoft Fabric
- Informatica
- Talend
- Collibra
- Alation
- Exposure to:
- AI/ML Data Platforms
- Generative AI Data Architecture
- Data Observability Platforms
- Familiarity with:
- DevOps CI/CD
- Infrastructure as Code (Terraform)
Soft Skills:
- Strong Leadership And Strategic Thinking Abilities.
- Excellent stakeholder management and communication skills.
- Ability to present architectural solutions to executive leadership.
- Strong analytical and problem-solving capabilities.
- Proven ability to lead large-scale enterprise transformation initiatives.
Educational Qualifications:
- Bachelor's or Master's Degree in:
- Computer Science
- Information Technology
- Data Engineering
- Engineering or related discipline
Preferred Certifications:
- TOGAF Certification
- Microsoft Certified: Azure Solutions Architect Expert
- Microsoft Certified: Azure Data Engineer Associate
- Databricks Certified Data Engineer Professional
- AWS Certified Solutions Architect Professional
- Google Professional Data Engineer
- Snowflake SnowPro Certification
Key Competencies:
- Enterprise Data Architecture
- Cloud Data Architecture
- Data Modeling
- Data Warehousing & Lakehouse Design
- Big Data Architecture
- Data Governance & MDM
- Data Security & Compliance
- Data Integration Architecture
- Stakeholder Management
- Technical Leadership & Mentoring
Ideal Candidate Profile:
- A highly experienced Data Architect with 12-15 years of experience in designing enterprise-scale data ecosystems, defining data strategy, architecting modern cloud-based data platforms, and leading large transformation programs.
- The candidate should possess exceptional expertise in data architecture, governance, data modeling, cloud platforms, and modern analytics solutions, along with the ability to provide strategic leadership and drive enterprise-wide data initiatives.
We need to decide which sections to output.
We have About the role (prose). Responsibilities (list). Requirements (maybe we combine Required Technical Skills + Preferred Skills + Soft Skills? But we have separate headings: Requirements, Qualifications, Skills. Could map:
- Requirements: Required Technical Skills (mandatory)
- Skills: Preferred Skills + Soft Skills (maybe)
- Qualifications: Educational Qualifications + Preferred Certifications + Key Competencies? But Key Competencies maybe more like skills. However we need to avoid thin sections. Could combine Qualifications and Certifications into one section? But we have only predefined headings; we cannot create new ones. So we need to decide which to use.
We could output:
About the role
...
Responsibilities
...
Requirements
...
(Required Technical Skills)
Skills
...
(Preferred Skills + Soft Skills)
Qualifications
...
(Educational Qualifications + Preferred Certifications + Key Competencies + Ideal Candidate Profile?) But Ideal Candidate Profile is prose summarizing; could be included as under Qualifications or maybe as part of About the role? But we already have About the role. Ideal Candidate Profile is similar to About the role but more specific; we could treat it as part of About the role? However we must preserve all info; we could add it as a separate paragraph under About the role (makes sense). But we should not duplicate content. We could merge Ideal Candidate Profile into About the role paragraph, but that would be summarizing? Actually we can just append it as another paragraph; still preserving.
But we need to avoid creating sections with filler. All sections we create have content.
Thus final HTML:
About the role
We are seeking an experienced Data Architect with 12-15 years of experience in designing and implementing enterprise-scale data platforms, data architectures, and data management solutions. The ideal candidate will possess strong expertise in data modeling, data warehousing, big data ecosystems, cloud data platforms, and modern data architecture frameworks. The Data Architect will be responsible for defining the organization's data strategy, designing scalable and secure data solutions, establishing best practices, and providing architectural leadership to data engineering, analytics, and business teams.
A highly experienced Data Architect with 12-15 years of experience in designing enterprise-scale data ecosystems, defining data strategy, architecting modern cloud-based data platforms, and leading large transformation programs. The candidate should possess exceptional expertise in data architecture, governance, data modeling, cloud platforms, and modern analytics solutions, along with the ability to provide strategic leadership and drive enterprise-wide data initiatives.
Responsibilities
- Define and implement enterprise data architecture strategy aligned with business objectives.
- Design scalable, secure, and high-performing data platforms supporting analytics, reporting, AI/ML, and operational workloads.
- Develop conceptual, logical, and physical data models for enterprise applications.
- Architect enterprise data warehouses, data lakes, lakehouses, and modern data platforms.
- Establish data governance, metadata management, data quality, and master data management frameworks.
- Lead the design of data integration and ETL/ELT architectures across multiple systems.
- Define standards, best practices, and architectural guidelines for data management.
- Collaborate with business leaders, enterprise architects, and technology teams to drive data transformation initiatives.
- Evaluate emerging technologies and recommend innovative data solutions.
- Provide technical leadership and mentorship to
for prose. Thus for each section we need to output
Heading
then eitherfor prose or
- for list.
We need to decide how to structure lists.
Let's extract content:
About the role (Role Overview):
"We are seeking an experienced Data Architect with 12-15 years of experience in designing and implementing enterprise-scale data platforms, data architectures, and data management solutions. The ideal candidate will possess strong expertise in data modeling, data warehousing, big data ecosystems, cloud data platforms, and modern data architecture frameworks. The Data Architect will be responsible for defining the organization's data strategy, designing scalable and secure data solutions, establishing best practices, and providing architectural leadership to data engineering, analytics, and business teams."
That's prose.
Responsibilities (Key Responsibilities):
List bullet points:
- Define and implement enterprise data architecture strategy aligned with business objectives.
- Design scalable, secure, and high-performing data platforms supporting analytics, reporting, AI/ML, and operational workloads.
- Develop conceptual, logical, and physical data models for enterprise applications.
- Architect enterprise data warehouses, data lakes, lakehouses, and modern data platforms.
- Establish data governance, metadata management, data quality, and master data management frameworks.
- Lead the design of data integration and ETL/ELT architectures across multiple systems.
- Define standards, best practices, and architectural guidelines for data management.
- Collaborate with business leaders, enterprise architects, and technology teams to drive data transformation initiatives.
- Evaluate emerging technologies and recommend innovative data solutions.
- Provide technical leadership and mentorship to data engineering and analytics teams.
- Ensure compliance with security, privacy, and regulatory requirements.
Requirements (Required Technical Skills):
We have many sub-bullets. Could structure as nested lists? Probably just flat list of items. But we need to preserve all detail. Could break into categories as subheadings? But only allowed headings are the predefined ones; we cannot add extra headings. So we need to put everything under Requirements as a list, maybe with sub-bullets using
- and within li we can include prose describing subcategories. That's acceptable.
We need to include all bullet points under Required Technical Skills.
Let's extract them:
Data Architecture & Design - Strong expertise in:
- Enterprise Data Architecture
- Data Strategy & Roadmaps
- Data Integration Architecture
- Information Architecture
- Reference Architecture Development
- Data Modernization Programs
Data Modeling - Extensive experience with:
- Conceptual Data Modeling
- Logical Data Modeling
- Physical Data Modeling
- Dimensional Modeling
- Data Vault 2.0
- Star & Snowflake Schemas
- Proficiency with data modeling tools such as:
- Erwin
- ER/Studio
- PowerDesigner
- Visio
Data Warehousing & Data Lakes - Strong experience designing:
- Enterprise Data Warehouses
- Data Lakes
- Lakehouse Architectures
- Operational Data Stores (ODS)
- Data Marts
- Deep understanding of:
- Data Lifecycle Management
- Metadata Management
- Data Lineage
Cloud & Modern Data Platforms - Strong experience in one or more cloud platforms:
Microsoft Azure:
- Azure Data Factory
- Azure Synapse Analytics
- Azure Data Lake Storage
- Microsoft Fabric
- Azure Databricks
Amazon Web Services (AWS):
- S3
- Glue
- Redshift
- EMR
- Athena
Google Cloud Platform (GCP):
- BigQuery
- Dataflow
- Dataproc
- Cloud Storage
Big Data & Data Engineering - Strong understanding of:
- Hadoop Ecosystem
- Spark / PySpark
- Hive
- Kafka
- Delta Lake
- Experience designing large-scale batch and real-time data processing architectures.
Database Technologies Experience Working With:
- SQL Server
- Oracle
- PostgreSQL
- Snowflake
- Teradata
- MySQL
- Strong Expertise In Database Design Query Optimization Data Partitioning Performance Tuning
Data Governance & Security - Strong knowledge of:
- Data Governance Frameworks
- Metadata Management
- Data Cataloging
- Data Quality Management
- Master Data Management (MDM)
- Data Privacy Regulations
- Experience implementing:
- Role-Based Access Control (RBAC)
- Data Security Policies
- Data Compliance Frameworks
Architecture & Integration Skills - Experience with:
- Enterprise Integration Patterns
- API-Based Data Integration
- Event-Driven Architecture
- Microservices Architecture
- Data Mesh Concepts
- Data Fabric Architecture
- Ability to create architecture artifacts, solution blueprints, and data flow diagrams.
Leadership Responsibilities (maybe part of Responsibilities? but we already have Responsibilities section; we could include leadership responsibilities there as part of responsibilities list. However the JD includes a separate "Leadership Responsibilities" bullet list after Required Technical Skills. We could incorporate those into Responsibilities as additional items. That would preserve all info.
Preferred Skills:
- Experience with:
- Snowflake
- Databricks
- Microsoft Fabric
- Informatica
- Talend
- Collibra
- Alation
- Exposure to:
- AI/ML Data Platforms
- Generative AI Data Architecture
- Data Observability Platforms
- Familiarity with:
- DevOps CI/CD
- Infrastructure as Code (Terraform)
Soft Skills:
- Strong Leadership And Strategic Thinking Abilities.
- Excellent stakeholder management and communication skills.
- Ability to present architectural solutions to executive leadership.
- Strong analytical and problem-solving capabilities.
- Proven ability to lead large-scale enterprise transformation initiatives.
Educational Qualifications:
- Bachelor's or Master's Degree in:
- Computer Science
- Information Technology
- Data Engineering
- Engineering or related discipline
Preferred Certifications:
- TOGAF Certification
- Microsoft Certified: Azure Solutions Architect Expert
- Microsoft Certified: Azure Data Engineer Associate
- Databricks Certified Data Engineer Professional
- AWS Certified Solutions Architect Professional
- Google Professional Data Engineer
- Snowflake SnowPro Certification
Key Competencies:
- Enterprise Data Architecture
- Cloud Data Architecture
- Data Modeling
- Data Warehousing & Lakehouse Design
- Big Data Architecture
- Data Governance & MDM
- Data Security & Compliance
- Data Integration Architecture
- Stakeholder Management
- Technical Leadership & Mentoring
Ideal Candidate Profile:
- A highly experienced Data Architect with 12-15 years of experience in designing enterprise-scale data ecosystems, defining data strategy, architecting modern cloud-based data platforms, and leading large transformation programs.
- The candidate should possess exceptional expertise in data architecture, governance, data modeling, cloud platforms, and modern analytics solutions, along with the ability to provide strategic leadership and drive enterprise-wide data initiatives.
We need to decide which sections to output.
We have About the role (prose). Responsibilities (list). Requirements (maybe we combine Required Technical Skills + Preferred Skills + Soft Skills? But we have separate headings: Requirements, Qualifications, Skills. Could map:
- Requirements: Required Technical Skills (mandatory)
- Skills: Preferred Skills + Soft Skills (maybe)
- Qualifications: Educational Qualifications + Preferred Certifications + Key Competencies? But Key Competencies maybe more like skills. However we need to avoid thin sections. Could combine Qualifications and Certifications into one section? But we have only predefined headings; we cannot create new ones. So we need to decide which to use.
We could output:
About the role
...
Responsibilities
- ...
Requirements
- ...
Skills
- ...
Qualifications
- ...
under Qualifications or maybe as part of About the role? But we already have About the role. Ideal Candidate Profile is similar to About the role but more specific; we could treat it as part of About the role? However we must preserve all info; we could add it as a separate paragraph under About the role (makes sense). But we should not duplicate content. We could merge Ideal Candidate Profile into About the role paragraph, but that would be summarizing? Actually we can just append it as another paragraph; still preserving. But we need to avoid creating sections with filler. All sections we create have content. Thus final HTML:
About the role
We are seeking an experienced Data Architect with 12-15 years of experience in designing and implementing enterprise-scale data platforms, data architectures, and data management solutions. The ideal candidate will possess strong expertise in data modeling, data warehousing, big data ecosystems, cloud data platforms, and modern data architecture frameworks. The Data Architect will be responsible for defining the organization's data strategy, designing scalable and secure data solutions, establishing best practices, and providing architectural leadership to data engineering, analytics, and business teams.
A highly experienced Data Architect with 12-15 years of experience in designing enterprise-scale data ecosystems, defining data strategy, architecting modern cloud-based data platforms, and leading large transformation programs. The candidate should possess exceptional expertise in data architecture, governance, data modeling, cloud platforms, and modern analytics solutions, along with the ability to provide strategic leadership and drive enterprise-wide data initiatives.
Responsibilities
- Define and implement enterprise data architecture strategy aligned with business objectives.
- Design scalable, secure, and high-performing data platforms supporting analytics, reporting, AI/ML, and operational workloads.
- Develop conceptual, logical, and physical data models for enterprise applications.
- Architect enterprise data warehouses, data lakes, lakehouses, and modern data platforms.
- Establish data governance, metadata management, data quality, and master data management frameworks.
- Lead the design of data integration and ETL/ELT architectures across multiple systems.
- Define standards, best practices, and architectural guidelines for data management.
- Collaborate with business leaders, enterprise architects, and technology teams to drive data transformation initiatives.
- Evaluate emerging technologies and recommend innovative data solutions.
- Provide technical leadership and mentorship to
- and within li we can include prose describing subcategories. That's acceptable.
We need to include all bullet points under Required Technical Skills.
Let's extract them:
Data Architecture & Design - Strong expertise in:
- Enterprise Data Architecture
- Data Strategy & Roadmaps
- Data Integration Architecture
- Information Architecture
- Reference Architecture Development
- Data Modernization Programs
Data Modeling - Extensive experience with:
- Conceptual Data Modeling
- Logical Data Modeling
- Physical Data Modeling
- Dimensional Modeling
- Data Vault 2.0
- Star & Snowflake Schemas
- Proficiency with data modeling tools such as:
- Erwin
- ER/Studio
- PowerDesigner
- Visio
Data Warehousing & Data Lakes - Strong experience designing:
- Enterprise Data Warehouses
- Data Lakes
- Lakehouse Architectures
- Operational Data Stores (ODS)
- Data Marts
- Deep understanding of:
- Data Lifecycle Management
- Metadata Management
- Data Lineage
Cloud & Modern Data Platforms - Strong experience in one or more cloud platforms:
Microsoft Azure:
- Azure Data Factory
- Azure Synapse Analytics
- Azure Data Lake Storage
- Microsoft Fabric
- Azure Databricks
Amazon Web Services (AWS):
- S3
- Glue
- Redshift
- EMR
- Athena
Google Cloud Platform (GCP):
- BigQuery
- Dataflow
- Dataproc
- Cloud Storage
Big Data & Data Engineering - Strong understanding of:
- Hadoop Ecosystem
- Spark / PySpark
- Hive
- Kafka
- Delta Lake
- Experience designing large-scale batch and real-time data processing architectures.
Database Technologies Experience Working With:
- SQL Server
- Oracle
- PostgreSQL
- Snowflake
- Teradata
- MySQL
- Strong Expertise In Database Design Query Optimization Data Partitioning Performance Tuning
Data Governance & Security - Strong knowledge of:
- Data Governance Frameworks
- Metadata Management
- Data Cataloging
- Data Quality Management
- Master Data Management (MDM)
- Data Privacy Regulations
- Experience implementing:
- Role-Based Access Control (RBAC)
- Data Security Policies
- Data Compliance Frameworks
Architecture & Integration Skills - Experience with:
- Enterprise Integration Patterns
- API-Based Data Integration
- Event-Driven Architecture
- Microservices Architecture
- Data Mesh Concepts
- Data Fabric Architecture
- Ability to create architecture artifacts, solution blueprints, and data flow diagrams.
Leadership Responsibilities (maybe part of Responsibilities? but we already have Responsibilities section; we could include leadership responsibilities there as part of responsibilities list. However the JD includes a separate "Leadership Responsibilities" bullet list after Required Technical Skills. We could incorporate those into Responsibilities as additional items. That would preserve all info.
Preferred Skills:
- Experience with:
- Snowflake
- Databricks
- Microsoft Fabric
- Informatica
- Talend
- Collibra
- Alation
- Exposure to:
- AI/ML Data Platforms
- Generative AI Data Architecture
- Data Observability Platforms
- Familiarity with:
- DevOps CI/CD
- Infrastructure as Code (Terraform)
Soft Skills:
- Strong Leadership And Strategic Thinking Abilities.
- Excellent stakeholder management and communication skills.
- Ability to present architectural solutions to executive leadership.
- Strong analytical and problem-solving capabilities.
- Proven ability to lead large-scale enterprise transformation initiatives.
Educational Qualifications:
- Bachelor's or Master's Degree in:
- Computer Science
- Information Technology
- Data Engineering
- Engineering or related discipline
Preferred Certifications:
- TOGAF Certification
- Microsoft Certified: Azure Solutions Architect Expert
- Microsoft Certified: Azure Data Engineer Associate
- Databricks Certified Data Engineer Professional
- AWS Certified Solutions Architect Professional
- Google Professional Data Engineer
- Snowflake SnowPro Certification
Key Competencies:
- Enterprise Data Architecture
- Cloud Data Architecture
- Data Modeling
- Data Warehousing & Lakehouse Design
- Big Data Architecture
- Data Governance & MDM
- Data Security & Compliance
- Data Integration Architecture
- Stakeholder Management
- Technical Leadership & Mentoring
Ideal Candidate Profile:
- A highly experienced Data Architect with 12-15 years of experience in designing enterprise-scale data ecosystems, defining data strategy, architecting modern cloud-based data platforms, and leading large transformation programs.
- The candidate should possess exceptional expertise in data architecture, governance, data modeling, cloud platforms, and modern analytics solutions, along with the ability to provide strategic leadership and drive enterprise-wide data initiatives.
We need to decide which sections to output.
We have About the role (prose). Responsibilities (list). Requirements (maybe we combine Required Technical Skills + Preferred Skills + Soft Skills? But we have separate headings: Requirements, Qualifications, Skills. Could map:
- Requirements: Required Technical Skills (mandatory)
- Skills: Preferred Skills + Soft Skills (maybe)
- Qualifications: Educational Qualifications + Preferred Certifications + Key Competencies? But Key Competencies maybe more like skills. However we need to avoid thin sections. Could combine Qualifications and Certifications into one section? But we have only predefined headings; we cannot create new ones. So we need to decide which to use.
We could output: