Manager, Data & Analytics
for each section heading. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to decide what to list as lists. Responsibilities: many bullet-like sentences separated by periods. Could turn each sentence into
- . Similarly Qualifications: list of bullet points. Benefits: maybe list? The benefits paragraph is prose; could keep as
. Pay: maybe
with salary range. Schedule:
with hybrid model.
We must preserve all substantive information; not invent or summarize away detail. So we need to include all duties, qualifications, preferred experience, success measures? Success Measures maybe could be part of Responsibilities or separate? Could be its own section? Not in list of headings but could be considered under Responsibilities or maybe a separate "Performance" but not required. We could include as part of Responsibilities or as a separate section "Success Measures" but not in prescribed headings; better to incorporate into Responsibilities or maybe as a separate section "Performance Expectations". However instruction: Prefer a few substantive sections over many thin ones. So we could merge Success Measures into Responsibilities or Qualifications? Probably Responsibilities.
Preferred Experience: could be part of Qualifications or separate "Preferred Experience" but not in headings list; we could incorporate into Qualifications as additional bullet points.
We need to ensure we don't include filler like "commensurate with experience". Not present.
Let's extract sections:
About the role: Position Summary paragraph(s). There's a paragraph: "Grant Thornton GTech’s vision is centered around enabling scalable, secure, and repeatable data integration capabilities that support enterprise transformation, mergers and acquisitions, and modern analytics. The Data & Analytics function is responsible for designing and delivering governed data solutions that connect critical business and technology systems while enabling trusted reporting, archival, and analytics at scale. The Manager, Data & Analytics will be responsible for designing, building, testing, and supporting data pipelines within enterprise standards that enable the integration of systems from acquired companies, as well as data archival in Snowflake. This role will work closely with data architecture, enterprise architecture, application teams, security, infrastructure, implementation partners, and business stakeholders to deliver reliable, governed, and reusable data integration capabilities that support M&A integration, operational continuity, compliance, reporting, analytics, and long-term data asset management."
That's the summary.
Responsibilities: list of Essential Duties & Responsibilities bullet points (starting with "Design, build, test, and support enterprise data pipelines..." etc). We'll turn each sentence into
- .
Qualifications & Requirements: list of bullet points (starting with "6+ years of experience..." etc). We'll turn each into
- .
Preferred Experience: could be added as additional bullet points under Qualifications or separate. Since we have Qualifications section, we can add them there as well.
Success Measures: could be added as bullet points under Responsibilities or as a separate "Performance Expectations" but we can incorporate into Responsibilities as additional bullet points.
Pay: base salary range $144,000-$200,000 plus description. We'll put in
.
Benefits: paragraph about personalized and comprehensive benefits, plus link. We'll keep as
.
Schedule: hybrid work model requiring in-person attendance at least three days per week. Also mention flex time off etc. We'll put as
.
We must not include application instructions, EEO, etc. Remove duplicated lines like "Benefits: We understand that your needs..." repeated. We'll keep one.
Now produce HTML fragment.
Structure:
About the role
...summary...
Responsibilities
- Design, build, test, and support enterprise data pipelines that enable integration of systems, applications, and data assets from acquired companies.
- Implement data ingestion, transformation, validation, and archival patterns that align with enterprise data architecture, security, governance, and operational standards.
- Support data archival solutions in Snowflake that enable acquired-company integration, historical reporting, data retention, compliance, and future analytics needs.
- Work with data architecture and enterprise architecture teams to ensure pipeline designs follow approved architecture patterns, naming conventions, metadata practices, access controls, and integration standards.
- Collaborate with M&A integration, application, infrastructure, security, and business teams to understand source-system requirements, data dependencies, integration timelines, archival needs, and target-state usage patterns.
- Translate business, technical, and integration requirements into data pipeline designs, data mappings, transformation logic, quality rules, and deployment activities.
- Develop and maintain scalable data engineering processes for batch, incremental, and event-driven data movement, where appropriate, across enterprise platforms and acquired-company systems.
- Implement data quality checks, reconciliation routines, exception handling, monitoring, and operational controls to ensure pipelines are reliable, auditable, and production-ready.
- Support data migration and archival activities by coordinating source extracts, transformation rules, target loading, validation outcomes, and issue resolution across internal and partner teams.
- Work with analytics and reporting stakeholders to ensure integrated and archived data assets are structured to support downstream business intelligence, analytics, AI, and operational reporting use cases.
- Coordinate with implementation vendors and technology teams to execute delivery activities, resolve blockers, validate deliverables, and ensure adherence to enterprise standards.
- Create and maintain technical documentation, including data flow diagrams, pipeline specifications, source-to-target mappings, operational runbooks, metadata, and support handoff materials.
- Support go-live readiness, cutover planning, data validation, post go-live hypercare, and transition of pipelines to steady-state data operations.
- Identify opportunities to improve data engineering patterns, automation, reuse, performance, cost efficiency, data observability, and operational supportability.
- Promote strong data engineering discipline, secure data handling, and consistent implementation practices within the Data & Analytics function.
Maybe also include success measures as additional li? Could add after responsibilities but maybe better to add as separate section "Success Measures". But not in headings list; but we could still include as Success Measures
if we want. The instruction: "Use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It says e.g., not exclusive. So we could add other headings if needed. But they said "include a section ONLY when the posting has real, specific content for it". Success Measures is specific content. So we could add a section for Success Measures. However they gave examples but not limited. Safer to add Success Measures as its own heading.
Similarly Preferred Experience could be its own section but we can incorporate into Qualifications.
Let's decide: We'll add Success Measures section.
Thus:
Success Measures
- Data pipelines for acquired-company system integration are delivered in alignment with enterprise standards, scope, timeline, and quality expectations.
- Snowflake archival solutions are reliable, governed, validated, discoverable, and structured to support retention, reporting, analytics, and future reuse.
- Source-to-target mappings, data quality checks, reconciliation outcomes, pipeline documentation, and operational handoffs are complete and supportable.
- Integration and archival activities protect business continuity during cutover, go-live, and post go-live hypercare.
- Data engineering patterns are reusable, secure, cost-aware, and aligned with enterprise data architecture and governance expectations.
Now Qualifications & Requirements: we can split into Requirements and Preferred Experience? But we have headings Requirements and Qualifications. Could use Requirements for mandatory, Qualifications for preferred? The instruction list includes Requirements and Qualifications separate. So we can do:
Requirements
- 6+ years of experience in data engineering, data integration, data architecture, analytics engineering, application integration, or related enterprise technology roles.
- Demonstrated experience designing, developing, and supporting production data pipelines using modern ETL/ELT, orchestration, data warehousing, data lake, or cloud data platform technologies.
- Hands-on experience with Snowflake, including data loading, transformation, schema design, performance considerations, access patterns, and operational support concepts.
- Experience integrating data from multiple enterprise systems, acquired-company systems, SaaS platforms, ERP systems, operational applications, or legacy technology environments.
- Working knowledge of source-to-target mapping, data transformation, data validation, reconciliation, data quality, metadata, lineage, and pipeline monitoring practices.
- Understanding of enterprise data architecture standards, data security, access management, privacy, data retention, archival patterns, and governed data delivery practices.
- Experience working with business stakeholders, application teams, architects, security, infrastructure, vendors, and delivery partners to translate requirements into implemented data solutions.
- Ability to work in a fast-paced integration environment with multiple priorities, dependencies, delivery milestones, and business continuity requirements.
- Strong problem-solving, communication, documentation, and stakeholder management skills, with the ability to explain technical concepts clearly to both technical and business audiences.
- Bachelor’s degree in Information Systems, Computer Science, Engineering, Data Analytics, Business, or related field required; relevant cloud/data certifications preferred.
Preferred Experience
- Experience supporting merger, acquisition, divestiture, or acquired-company technology integration programs.
- Experience with cloud data platforms and tools such as Snowflake, Azure Data Factory, Databricks, dbt, Informatica, Fivetran, Matillion, Airflow, or comparable data engineering technologies.
- Experience with data archival, historical data retention, legacy system decommissioning, or compliance-driven data preservation initiatives.
- Familiarity with analytics and reporting platforms such as Power BI, Tableau, Qlik, or comparable business intelligence tools.
- Experience operating in a professional services, consulting, accounting, or complex global enterprise environment.
- Experience contributing to reusable pipeline frameworks, engineering standards, data platform runbooks, and operational support models.
Now Benefits: we have a paragraph about personalized and comprehensive benefits plus link. We'll output as
.
Pay:
The base salary range for this position is between $144,000 and $200,000. Placement within the pay range is at Grant Thornton’s discretion, and it is based on multiple factors, including but not limited to, job-related knowledge/skills, experience, business needs, progression within the role, geographic location, and internal equity. At Grant Thornton, compensation decisions are dependent upon the facts and circumstances of each position and candidate.
Schedule: Consistent with the firm's hybrid work model, this position will require in-person attendance at least three days per week, either at a GT office or client site. The role offers flexibility in work location, weekly schedule, and flex time off to support work-life integration.
We should also include
for prose. We need to decide what to list as lists. Responsibilities: many bullet-like sentences separated by periods. Could turn each sentence into
. Pay: maybe
with salary range. Schedule:
with hybrid model. We must preserve all substantive information; not invent or summarize away detail. So we need to include all duties, qualifications, preferred experience, success measures? Success Measures maybe could be part of Responsibilities or separate? Could be its own section? Not in list of headings but could be considered under Responsibilities or maybe a separate "Performance" but not required. We could include as part of Responsibilities or as a separate section "Success Measures" but not in prescribed headings; better to incorporate into Responsibilities or maybe as a separate section "Performance Expectations". However instruction: Prefer a few substantive sections over many thin ones. So we could merge Success Measures into Responsibilities or Qualifications? Probably Responsibilities. Preferred Experience: could be part of Qualifications or separate "Preferred Experience" but not in headings list; we could incorporate into Qualifications as additional bullet points. We need to ensure we don't include filler like "commensurate with experience". Not present. Let's extract sections: About the role: Position Summary paragraph(s). There's a paragraph: "Grant Thornton GTech’s vision is centered around enabling scalable, secure, and repeatable data integration capabilities that support enterprise transformation, mergers and acquisitions, and modern analytics. The Data & Analytics function is responsible for designing and delivering governed data solutions that connect critical business and technology systems while enabling trusted reporting, archival, and analytics at scale. The Manager, Data & Analytics will be responsible for designing, building, testing, and supporting data pipelines within enterprise standards that enable the integration of systems from acquired companies, as well as data archival in Snowflake. This role will work closely with data architecture, enterprise architecture, application teams, security, infrastructure, implementation partners, and business stakeholders to deliver reliable, governed, and reusable data integration capabilities that support M&A integration, operational continuity, compliance, reporting, analytics, and long-term data asset management." That's the summary. Responsibilities: list of Essential Duties & Responsibilities bullet points (starting with "Design, build, test, and support enterprise data pipelines..." etc). We'll turn each sentence into
. Benefits: paragraph about personalized and comprehensive benefits, plus link. We'll keep as
. Schedule: hybrid work model requiring in-person attendance at least three days per week. Also mention flex time off etc. We'll put as
. We must not include application instructions, EEO, etc. Remove duplicated lines like "Benefits: We understand that your needs..." repeated. We'll keep one. Now produce HTML fragment. Structure:
About the role
...summary...
Responsibilities
- Design, build, test, and support enterprise data pipelines that enable integration of systems, applications, and data assets from acquired companies.
- Implement data ingestion, transformation, validation, and archival patterns that align with enterprise data architecture, security, governance, and operational standards.
- Support data archival solutions in Snowflake that enable acquired-company integration, historical reporting, data retention, compliance, and future analytics needs.
- Work with data architecture and enterprise architecture teams to ensure pipeline designs follow approved architecture patterns, naming conventions, metadata practices, access controls, and integration standards.
- Collaborate with M&A integration, application, infrastructure, security, and business teams to understand source-system requirements, data dependencies, integration timelines, archival needs, and target-state usage patterns.
- Translate business, technical, and integration requirements into data pipeline designs, data mappings, transformation logic, quality rules, and deployment activities.
- Develop and maintain scalable data engineering processes for batch, incremental, and event-driven data movement, where appropriate, across enterprise platforms and acquired-company systems.
- Implement data quality checks, reconciliation routines, exception handling, monitoring, and operational controls to ensure pipelines are reliable, auditable, and production-ready.
- Support data migration and archival activities by coordinating source extracts, transformation rules, target loading, validation outcomes, and issue resolution across internal and partner teams.
- Work with analytics and reporting stakeholders to ensure integrated and archived data assets are structured to support downstream business intelligence, analytics, AI, and operational reporting use cases.
- Coordinate with implementation vendors and technology teams to execute delivery activities, resolve blockers, validate deliverables, and ensure adherence to enterprise standards.
- Create and maintain technical documentation, including data flow diagrams, pipeline specifications, source-to-target mappings, operational runbooks, metadata, and support handoff materials.
- Support go-live readiness, cutover planning, data validation, post go-live hypercare, and transition of pipelines to steady-state data operations.
- Identify opportunities to improve data engineering patterns, automation, reuse, performance, cost efficiency, data observability, and operational supportability.
- Promote strong data engineering discipline, secure data handling, and consistent implementation practices within the Data & Analytics function.
Success Measures
if we want. The instruction: "Use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It says e.g., not exclusive. So we could add other headings if needed. But they said "include a section ONLY when the posting has real, specific content for it". Success Measures is specific content. So we could add a section for Success Measures. However they gave examples but not limited. Safer to add Success Measures as its own heading.
Similarly Preferred Experience could be its own section but we can incorporate into Qualifications.
Let's decide: We'll add Success Measures section.
Thus:
Success Measures
- Data pipelines for acquired-company system integration are delivered in alignment with enterprise standards, scope, timeline, and quality expectations.
- Snowflake archival solutions are reliable, governed, validated, discoverable, and structured to support retention, reporting, analytics, and future reuse.
- Source-to-target mappings, data quality checks, reconciliation outcomes, pipeline documentation, and operational handoffs are complete and supportable.
- Integration and archival activities protect business continuity during cutover, go-live, and post go-live hypercare.
- Data engineering patterns are reusable, secure, cost-aware, and aligned with enterprise data architecture and governance expectations.
Now Qualifications & Requirements: we can split into Requirements and Preferred Experience? But we have headings Requirements and Qualifications. Could use Requirements for mandatory, Qualifications for preferred? The instruction list includes Requirements and Qualifications separate. So we can do:
Requirements
- 6+ years of experience in data engineering, data integration, data architecture, analytics engineering, application integration, or related enterprise technology roles.
- Demonstrated experience designing, developing, and supporting production data pipelines using modern ETL/ELT, orchestration, data warehousing, data lake, or cloud data platform technologies.
- Hands-on experience with Snowflake, including data loading, transformation, schema design, performance considerations, access patterns, and operational support concepts.
- Experience integrating data from multiple enterprise systems, acquired-company systems, SaaS platforms, ERP systems, operational applications, or legacy technology environments.
- Working knowledge of source-to-target mapping, data transformation, data validation, reconciliation, data quality, metadata, lineage, and pipeline monitoring practices.
- Understanding of enterprise data architecture standards, data security, access management, privacy, data retention, archival patterns, and governed data delivery practices.
- Experience working with business stakeholders, application teams, architects, security, infrastructure, vendors, and delivery partners to translate requirements into implemented data solutions.
- Ability to work in a fast-paced integration environment with multiple priorities, dependencies, delivery milestones, and business continuity requirements.
- Strong problem-solving, communication, documentation, and stakeholder management skills, with the ability to explain technical concepts clearly to both technical and business audiences.
- Bachelor’s degree in Information Systems, Computer Science, Engineering, Data Analytics, Business, or related field required; relevant cloud/data certifications preferred.
Preferred Experience
- Experience supporting merger, acquisition, divestiture, or acquired-company technology integration programs.
- Experience with cloud data platforms and tools such as Snowflake, Azure Data Factory, Databricks, dbt, Informatica, Fivetran, Matillion, Airflow, or comparable data engineering technologies.
- Experience with data archival, historical data retention, legacy system decommissioning, or compliance-driven data preservation initiatives.
- Familiarity with analytics and reporting platforms such as Power BI, Tableau, Qlik, or comparable business intelligence tools.
- Experience operating in a professional services, consulting, accounting, or complex global enterprise environment.
- Experience contributing to reusable pipeline frameworks, engineering standards, data platform runbooks, and operational support models.
Now Benefits: we have a paragraph about personalized and comprehensive benefits plus link. We'll output as
. Pay:
The base salary range for this position is between $144,000 and $200,000. Placement within the pay range is at Grant Thornton’s discretion, and it is based on multiple factors, including but not limited to, job-related knowledge/skills, experience, business needs, progression within the role, geographic location, and internal equity. At Grant Thornton, compensation decisions are dependent upon the facts and circumstances of each position and candidate.
Schedule:Consistent with the firm's hybrid work model, this position will require in-person attendance at least three days per week, either at a GT office or client site. The role offers flexibility in work location, weekly schedule, and flex time off to support work-life integration.
We should also include