Data Engineering - Associate
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. Remove boilerplate, navigation, legal, EEO, equal-opportunity, scam-warning, duplicated lines, application instructions. Preserve all substantive information; do not invent or summarize away detail. Include a section ONLY when posting has real, specific content for it — never emit heading followed by filler/placeholder text. Prefer few substantive sections over many thin ones.
We need to parse the posting and decide which sections to include.
The posting includes:
- Intro paragraph: "Build reliable data foundations that help clients turn complex data into trusted, actionable insights. We are seeking a highly motivated Data Engineering Consulting Associate to join our Data & Analytics practice. In this role, you will help clients design, build, and optimize modern data platforms that enable advanced analytics, reporting, and AI-driven decision-making. You will work alongside experienced consultants, solution architects, and client stakeholders to deliver end-to-end data engineering solutions across cloud and on-premises environments. This position offers an excellent opportunity to develop both technical expertise and consulting skills while working on large-scale digital transformation initiatives. We're anticipating future openings and are connecting now with great talent who'd like to be considered when the time comes."
That could be "About the role" or "Overview". We can use
About the role
with containing that paragraph.
- "What You'll Work On As an Associate, Data Engineer, you will:" then a list of duties. That's responsibilities. So
Responsibilities
with - each bullet.
- "What You'll Bring You will likely be successful in this role if you have:" then list of qualifications/requirements. That's Requirements/Qualifications. Could combine as
Requirements
with list.
- "How You Work" paragraph describing traits. Could be How You Work
with .
- "Bonus Points If You Have" list of bonus skills/experience. Could be
Preferred Qualifications
or Bonus Points
. Use Bonus Points
with list.
- "What Success Looks Like" paragraphs describing expectations. Could be What Success Looks Like
with (maybe multiple paragraphs). Could treat as section.
- "Why Join FormativGroup" paragraph about company. Could be
Why Join FormativGroup
with .
- "Employment Eligibility" paragraph about work authorization, remote role, travel, location restrictions. That's substantive; could be
Employment Eligibility
with .
- "We are committed to pay equity and transparency." then compensation range etc. That's Pay and Benefits. So we need Pay section if actual numbers given. Yes: "Estimated Compensation Range: $75,000—$85,000 USD". Also mention discretionary bonuses, commissions, benefits package. So we can have
Pay
with containing that. Benefits: mention comprehensive benefits package includes medical, dental, vision, 401(k), paid time off, etc. That's Benefits section. Could combine Pay and Benefits? The instruction: prefer few substantive sections over many thin ones. But we have both Pay and Benefits distinct. Could combine into one section "Compensation and Benefits". However headings list includes Pay, Benefits, Schedule. We could create a single section "Compensation and Benefits" but that's not exactly one of the headings; but we can still use
Compensation and Benefits
as it's not prohibited? The instruction says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It gives examples but not exclusive list. It says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Likely they expect those headings if applicable. But we can also use other headings if needed? It says "use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." That suggests we should use those headings for sections. It doesn't forbid other headings, but to be safe we should stick to those. However we have many sections: About the role, Responsibilities, Requirements, How You Work, Bonus Points, What Success Looks Like, Why Join FormativGroup, Employment Eligibility, Pay, Benefits, Schedule? Schedule not present. We could combine some.
We need to decide which sections to include based on content.
We have:
- About the role: yes.
- Responsibilities: yes.
- Requirements: yes (could be Requirements or Qualifications). We'll use Requirements.
- Skills: maybe we could include Skills from "Bonus Points" but that's more preferred qualifications. Could treat as Skills.
- Benefits: yes (benefits package).
- Pay: yes (compensation range).
- Schedule: not mentioned (no schedule details). So omit.
- Qualifications: we already used Requirements; could also have Qualifications but duplicate.
We need to avoid filler/placeholder text. All content we have is substantive.
We need to remove boilerplate: EEO, equal-opportunity, scam-warning, privacy policy, application instructions like "Click here to view our Privacy Policy", "#formativgroupcareers #WorkWithUs", etc. Also remove "Employment Eligibility" maybe not boilerplate but it's substantive (location restrictions, travel). Keep.
We need to preserve all substantive information: location list, travel %, remote, work authorization, etc.
We need to output only HTML fragment, no /, no markdown fences.
We'll structure:
About the role
...
Responsibilities
- ...
...
Requirements
- ...
...
How You Work
...
Bonus Points
- ...
...
What Success Looks Like
...
Why Join FormativGroup
...
Employment Eligibility
...
Pay
...
Benefits
...
We need to ensure we don't repeat content. Let's extract each piece.
First, About the role paragraph: "Build reliable data foundations that help clients turn complex data into trusted, actionable insights. We are seeking a highly motivated Data Engineering Consulting Associate to join our Data & Analytics practice. In this role, you will help clients design, build, and optimize modern data platforms that enable advanced analytics, reporting, and AI-driven decision-making. You will work alongside experienced consultants, solution architects, and client stakeholders to deliver end-to-end data engineering solutions across cloud and on-premises environments. This position offers an excellent opportunity to develop both technical expertise and consulting skills while working on large-scale digital transformation initiatives. We're anticipating future openings and are connecting now with great talent who'd like to be considered when the time comes."
We'll put that in .
Responsibilities: list after "What You'll Work On As an Associate, Data Engineer, you will:" then bullet points each starting with a verb. Let's capture them:
- Design, develop, test, and maintain scalable data pipelines and ETL/ELT processes.
- Support the implementation of modern data platforms, data lakes, and data warehouses.
- Develop data integration solutions using cloud-native and enterprise technologies.
- Build and optimize data ingestion frameworks for structured and unstructured data.
- Perform data quality validation, transformation, reconciliation, and performance tuning.
- Support migration of legacy data environments to modern cloud architectures.
- Gather business and technical requirements from client stakeholders.
- Translate business needs into practical technical data solutions.
- Participate in workshops, design sessions, demonstrations, and project status meetings.
- Document technical designs, architecture decisions, operating procedures, and development standards.
- Support disciplined project delivery across one or more client workstreams.
- Collaborate with business intelligence, analytics, AI, and data science teams to ensure reliable data availability.
- Support the development of curated data models for reporting and analytical use cases.
- Assist with implementing data governance, security, privacy, and compliance controls.
- Identify opportunities to improve platform performance, scalability, reliability, and maintainability.
- Stay current on emerging cloud, data engineering, analytics, and AI technologies.
- Contribute to internal accelerators, reusable assets, proposals, and knowledge-sharing initiatives.
We'll put each as
- .
Requirements: after "What You'll Bring You will likely be successful in this role if you have:" then bullet points:
- Bachelor's degree in Computer Science, Information Systems, Data Engineering, Engineering, Mathematics, or a related field.
- 1-3 years of experience in data engineering, data integration, analytics engineering, software engineering, or a related technical role.
- Hands-on experience with SQL, relational databases, and ETL/ELT development.
- Understanding of data warehousing concepts, dimensional modeling, and data quality practices.
- Experience with at least one programming language such as Python, Scala, or Java.
- Familiarity with at least one cloud platform such as Microsoft Azure, AWS, or Google Cloud.
- Strong analytical, problem-solving, communication, and documentation skills.
- Ability to work collaboratively in a team-oriented, client-facing environment.
We'll put each as
- .
How You Work: paragraph: "You are curious, practical, collaborative, detail-oriented, and motivated to learn through feedback and hands-on delivery.You handle ambiguity by clarifying assumptions, gathering evidence, communicating progress, and escalating thoughtfully when needed.You balance pace with quality and treat testing, documentation, reliability, security, and maintainability as part of the work."
Note there are no spaces after periods; we can keep as is or add spaces for readability but must preserve substance. We'll keep as is but maybe add a space after each period for readability; but that's altering text slightly. Better to keep exactly as given? The instruction: preserve ALL substantive information; do NOT invent or summarize away detail. Adding spaces is not changing substantive info, just formatting. It's okay. We'll keep as is but maybe add a space after each period to avoid run-on sentences; but it's not required. I'll keep original but add a space after each period for readability; it's not changing meaning. Probably fine.
Bonus Points: after "Bonus Points If You Have Experience with Azure Data Factory, Microsoft Fabric, Databricks, Synapse Analytics, Snowflake, Redshift, BigQuery, Airflow, dbt, or comparable platforms. Knowledge of distributed data processing frameworks such as Apache Spark. Exposure to data governance, metadata management, master data management, or data observability. Experience in consulting, professional services, or another client-facing delivery environment. Relevant certifications from Microsoft Azure, AWS, Google Cloud, Databricks, or Snowflake."
We need to convert to list items. Each sentence can be a bullet.
- Experience with Azure Data Factory, Microsoft Fabric, Databricks, Synapse Analytics, Snowflake, Redshift, BigQuery, Airflow, dbt, or comparable platforms.
- Knowledge of distributed data processing frameworks such as Apache Spark.
- Exposure to data governance, metadata management, master data management, or data observability.
- Experience in consulting, professional services, or another client-facing delivery environment.
- Relevant certifications from
for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Remove boilerplate, navigation, legal, EEO, equal-opportunity, scam-warning, duplicated lines, application instructions. Preserve all substantive information; do not invent or summarize away detail. Include a section ONLY when posting has real, specific content for it — never emit heading followed by filler/placeholder text. Prefer few substantive sections over many thin ones. We need to parse the posting and decide which sections to include. The posting includes: - Intro paragraph: "Build reliable data foundations that help clients turn complex data into trusted, actionable insights. We are seeking a highly motivated Data Engineering Consulting Associate to join our Data & Analytics practice. In this role, you will help clients design, build, and optimize modern data platforms that enable advanced analytics, reporting, and AI-driven decision-making. You will work alongside experienced consultants, solution architects, and client stakeholders to deliver end-to-end data engineering solutions across cloud and on-premises environments. This position offers an excellent opportunity to develop both technical expertise and consulting skills while working on large-scale digital transformation initiatives. We're anticipating future openings and are connecting now with great talent who'd like to be considered when the time comes." That could be "About the role" or "Overview". We can use
About the role
withcontaining that paragraph. - "What You'll Work On As an Associate, Data Engineer, you will:" then a list of duties. That's responsibilities. So
Responsibilities
with- each bullet.
- "What You'll Bring You will likely be successful in this role if you have:" then list of qualifications/requirements. That's Requirements/Qualifications. Could combine as
Requirements
with list. - "How You Work" paragraph describing traits. Could beHow You Work
with. - "Bonus Points If You Have" list of bonus skills/experience. Could be
Preferred Qualifications
orBonus Points
. UseBonus Points
with list. - "What Success Looks Like" paragraphs describing expectations. Could beWhat Success Looks Like
with(maybe multiple paragraphs). Could treat as section. - "Why Join FormativGroup" paragraph about company. Could be
Why Join FormativGroup
with. - "Employment Eligibility" paragraph about work authorization, remote role, travel, location restrictions. That's substantive; could be
Employment Eligibility
with. - "We are committed to pay equity and transparency." then compensation range etc. That's Pay and Benefits. So we need Pay section if actual numbers given. Yes: "Estimated Compensation Range: $75,000—$85,000 USD". Also mention discretionary bonuses, commissions, benefits package. So we can have
Pay
withcontaining that. Benefits: mention comprehensive benefits package includes medical, dental, vision, 401(k), paid time off, etc. That's Benefits section. Could combine Pay and Benefits? The instruction: prefer few substantive sections over many thin ones. But we have both Pay and Benefits distinct. Could combine into one section "Compensation and Benefits". However headings list includes Pay, Benefits, Schedule. We could create a single section "Compensation and Benefits" but that's not exactly one of the headings; but we can still use
Compensation and Benefits
as it's not prohibited? The instruction says usefor section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It gives examples but not exclusive list. It says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Likely they expect those headings if applicable. But we can also use other headings if needed? It says "use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." That suggests we should use those headings for sections. It doesn't forbid other headings, but to be safe we should stick to those. However we have many sections: About the role, Responsibilities, Requirements, How You Work, Bonus Points, What Success Looks Like, Why Join FormativGroup, Employment Eligibility, Pay, Benefits, Schedule? Schedule not present. We could combine some. We need to decide which sections to include based on content. We have: - About the role: yes. - Responsibilities: yes. - Requirements: yes (could be Requirements or Qualifications). We'll use Requirements. - Skills: maybe we could include Skills from "Bonus Points" but that's more preferred qualifications. Could treat as Skills. - Benefits: yes (benefits package). - Pay: yes (compensation range). - Schedule: not mentioned (no schedule details). So omit. - Qualifications: we already used Requirements; could also have Qualifications but duplicate. We need to avoid filler/placeholder text. All content we have is substantive. We need to remove boilerplate: EEO, equal-opportunity, scam-warning, privacy policy, application instructions like "Click here to view our Privacy Policy", "#formativgroupcareers #WorkWithUs", etc. Also remove "Employment Eligibility" maybe not boilerplate but it's substantive (location restrictions, travel). Keep. We need to preserve all substantive information: location list, travel %, remote, work authorization, etc. We need to output only HTML fragment, no /, no markdown fences. We'll structure:
About the role
...
Responsibilities
- ... ...
Requirements
- ... ...
How You Work
...
Bonus Points
- ... ...
What Success Looks Like
...
Why Join FormativGroup
...
Employment Eligibility
...
Pay
...
Benefits
...
We need to ensure we don't repeat content. Let's extract each piece. First, About the role paragraph: "Build reliable data foundations that help clients turn complex data into trusted, actionable insights. We are seeking a highly motivated Data Engineering Consulting Associate to join our Data & Analytics practice. In this role, you will help clients design, build, and optimize modern data platforms that enable advanced analytics, reporting, and AI-driven decision-making. You will work alongside experienced consultants, solution architects, and client stakeholders to deliver end-to-end data engineering solutions across cloud and on-premises environments. This position offers an excellent opportunity to develop both technical expertise and consulting skills while working on large-scale digital transformation initiatives. We're anticipating future openings and are connecting now with great talent who'd like to be considered when the time comes." We'll put that in. Responsibilities: list after "What You'll Work On As an Associate, Data Engineer, you will:" then bullet points each starting with a verb. Let's capture them: - Design, develop, test, and maintain scalable data pipelines and ETL/ELT processes. - Support the implementation of modern data platforms, data lakes, and data warehouses. - Develop data integration solutions using cloud-native and enterprise technologies. - Build and optimize data ingestion frameworks for structured and unstructured data. - Perform data quality validation, transformation, reconciliation, and performance tuning. - Support migration of legacy data environments to modern cloud architectures. - Gather business and technical requirements from client stakeholders. - Translate business needs into practical technical data solutions. - Participate in workshops, design sessions, demonstrations, and project status meetings. - Document technical designs, architecture decisions, operating procedures, and development standards. - Support disciplined project delivery across one or more client workstreams. - Collaborate with business intelligence, analytics, AI, and data science teams to ensure reliable data availability. - Support the development of curated data models for reporting and analytical use cases. - Assist with implementing data governance, security, privacy, and compliance controls. - Identify opportunities to improve platform performance, scalability, reliability, and maintainability. - Stay current on emerging cloud, data engineering, analytics, and AI technologies. - Contribute to internal accelerators, reusable assets, proposals, and knowledge-sharing initiatives. We'll put each as
- . Requirements: after "What You'll Bring You will likely be successful in this role if you have:" then bullet points: - Bachelor's degree in Computer Science, Information Systems, Data Engineering, Engineering, Mathematics, or a related field. - 1-3 years of experience in data engineering, data integration, analytics engineering, software engineering, or a related technical role. - Hands-on experience with SQL, relational databases, and ETL/ELT development. - Understanding of data warehousing concepts, dimensional modeling, and data quality practices. - Experience with at least one programming language such as Python, Scala, or Java. - Familiarity with at least one cloud platform such as Microsoft Azure, AWS, or Google Cloud. - Strong analytical, problem-solving, communication, and documentation skills. - Ability to work collaboratively in a team-oriented, client-facing environment. We'll put each as
- . How You Work: paragraph: "You are curious, practical, collaborative, detail-oriented, and motivated to learn through feedback and hands-on delivery.You handle ambiguity by clarifying assumptions, gathering evidence, communicating progress, and escalating thoughtfully when needed.You balance pace with quality and treat testing, documentation, reliability, security, and maintainability as part of the work." Note there are no spaces after periods; we can keep as is or add spaces for readability but must preserve substance. We'll keep as is but maybe add a space after each period for readability; but that's altering text slightly. Better to keep exactly as given? The instruction: preserve ALL substantive information; do NOT invent or summarize away detail. Adding spaces is not changing substantive info, just formatting. It's okay. We'll keep as is but maybe add a space after each period to avoid run-on sentences; but it's not required. I'll keep original but add a space after each period for readability; it's not changing meaning. Probably fine. Bonus Points: after "Bonus Points If You Have Experience with Azure Data Factory, Microsoft Fabric, Databricks, Synapse Analytics, Snowflake, Redshift, BigQuery, Airflow, dbt, or comparable platforms. Knowledge of distributed data processing frameworks such as Apache Spark. Exposure to data governance, metadata management, master data management, or data observability. Experience in consulting, professional services, or another client-facing delivery environment. Relevant certifications from Microsoft Azure, AWS, Google Cloud, Databricks, or Snowflake." We need to convert to list items. Each sentence can be a bullet. - Experience with Azure Data Factory, Microsoft Fabric, Databricks, Synapse Analytics, Snowflake, Redshift, BigQuery, Airflow, dbt, or comparable platforms. - Knowledge of distributed data processing frameworks such as Apache Spark. - Exposure to data governance, metadata management, master data management, or data observability. - Experience in consulting, professional services, or another client-facing delivery environment. - Relevant certifications from