Data Engineer, Sales & Service
for each heading. Use - for any list of duties/requirements/benefits. Use
for prose. Do not repeat job title as heading. Do not include Job ID etc. Remove boilerplate: EEO, equal opportunity, scam warning, navigation, cookie, legal, duplicated lines, application instructions. Also we should not create sections unless posting has real specific content. We have content for About the role (first paragraph). Responsibilities (the "In This Role You will ..." bullet list). Requirements (the "What You Need To Succeed" list). Qualifications? Might be overlapping with Requirements; but we can treat Requirements as required qualifications. Qualifications maybe for preferred? The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Skills). We need to decide which sections to include based on content. We have:
- About the role: first paragraph describing role, hybrid, team, why matters.
- Responsibilities: bullet list of duties.
- Requirements: list of required items (the "What You Need To Succeed" required items). Also there are "Strongly Preferred" and "Preferred". Those could go under Qualifications or Skills. The instruction: Use sections only when posting has real specific content. We have content for required, preferred, skills. Could combine into Requirements (required) and Qualifications (preferred) and Skills (maybe technical skills). But we have many items overlapping.
Better to follow typical structure: About the role, Responsibilities, Requirements (required qualifications), Qualifications (preferred), Skills (maybe technical skills), Benefits, Pay, Schedule.
We have Pay: "Salary range starting at $70,000 based on experience". Also mention annual cash bonus. That's pay info.
Schedule: hybrid role based out of Westbrook, Maine headquarters, expectation of 2 days onsite per week. That's schedule.
Benefits: list of benefits: Health/Dental/Vision, Day-One 5% matching 401k, additional benefits including financial support, pet insurance, mental health resources, volunteer paid days off, employee stock program, foundation donation matching, etc.
We need to avoid filler like "commensurate with experience" etc. We have actual numbers.
We must not include duplicated lines. Also remove EEO etc.
We must output only HTML fragment, no /, no markdown fences.
Let's extract content.
About the role: combine first few paragraphs up to "Why does this role matter?" maybe include that as part of About. Let's capture:
"As a Data Engineer, you will help build and scale the data infrastructure that powers analytics, reporting, AI-enabled products, and business decision-making across IDEXX. This is a hybrid role based out of our Westbrook, Maine headquarters, with an expectation of 2 days onsite per week. The Global Sales and Service Analytics team develops and supports the data products, governance frameworks, analytical solutions, and AI capabilities that empower stakeholders ranging from Sales Professionals to Executive Leadership around the world. Why does this role matter? This role sits at the intersection of modern data engineering and artificial intelligence, giving you the opportunity to directly influence how data is transformed into insights, products, and business outcomes. You'll join a highly collaborative team working with a modern cloud and AI technology stack to develop scalable data pipelines, support advanced analytics, and build AI-enabled solutions. Your work will have visibility across the organization and play a key role in advancing data-driven decision making, innovation, and operational excellence. This is an excellent opportunity for an early-career data engineer to expand technical expertise, take on increasing ownership, and help shape the future of data and AI at IDEXX."
That seems good.
Responsibilities: list each bullet starting with "You will". We'll extract each sentence.
List:
- design and build scalable, reliable data pipelines that support analytics, AI applications, dashboards, and enterprise reporting solutions.
- develop and maintain SQL and Python code that enables efficient data transformation, integration, testing, and validation.
- contribute to the development and governance of enterprise data models that promote consistency, scalability, and reuse across multiple products and solutions.
- help manage automated deployment processes and support continuous integration and delivery practices for data engineering solutions.
- partner with analysts, developers, and business stakeholders to understand data requirements and deliver trusted data assets.
- support data architecture initiatives by helping define standards for how data is stored, integrated, accessed, and managed within cloud environments.
- apply AI and large language model technologies within data pipelines and analytical products to improve efficiency and unlock new capabilities.
- build and enhance AI-powered agents and solutions that support business users and analytical workflows.
- participate in code reviews and contribute to data governance practices that promote quality, maintainability, and consistency.
- monitor and optimize pipeline performance, troubleshoot issues, and implement improvements aligned with team standards.
- resolve production issues, enhancements, and development requests while balancing evolving business priorities.
- collaborate closely with both technical and non-technical stakeholders to deliver impactful data solutions.
We'll put each as
- .
Requirements (required): list of required items from "What You Need To Succeed" required (those with "(Required)").
Let's extract:
- proficiency in SQL and Python, with experience building and maintaining data pipelines and data infrastructure.
- Bachelor's degree or an equivalent combination of education and experience.
- experience with data transformation and data modeling tools, including dbt or similar technologies.
- knowledge of data warehousing concepts and dimensional modeling practices.
- experience working with cloud-based data platforms and modern data ecosystems.
- experience using version control systems and CI/CD practices to support software or data engineering development.
- strong collaboration and communication skills, enabling effective partnership with technical and non-technical stakeholders.
- demonstrate attention to data quality, governance, and continuous improvement in a fast-paced environment.
- strong organizational, prioritization, and problem-solving abilities.
That's 9 items.
Qualifications (preferred/strongly preferred): we have "Strongly Preferred" and "Preferred". Could combine into Qualifications.
List:
Strongly Preferred:
- 1 to 3 years of relevant data engineering experience and are eager to expand your technical expertise and ownership.
- experience with dbt for data transformation and modeling.
- experience with workflow orchestration tools such as Dagster.
- experience working within AWS and Snowflake environments.
- experience building and supporting CI/CD pipelines using GitHub and GitHub Actions.
- familiarity with software APIs and data integration patterns.
- experience working in Agile or Scrum development environments.
Preferred:
- exposure to AI, machine learning, or large language model technologies and are excited about applying them to data products and workflows.
- experience with Databricks, Snowflake Cortex, AI agents, or similar AI-enabled platforms.
- demonstrate intellectual curiosity, a bias toward automation, and enthusiasm for modern data engineering technologies.
- experience supporting data governance initiatives, code reviews, or technical mentorship activities.
We'll list them as bullet points under Qualifications.
Skills: maybe we could separate technical skills but we already covered many. Could omit Skills section if redundant. But instruction: prefer a few substantive sections over many thin ones. We could combine Skills into Requirements/Qualifications. However we have to decide if we need Skills heading. Since we already have technical skills covered, we could omit Skills to avoid thin section. But we need to include sections only when posting has real specific content. We have content for skills (like SQL, Python, etc.) but they are already in Requirements. So we can skip Skills.
Benefits: list:
- Health / Dental / Vision Benefits
- Day-One 5% matching 401k
- Additional benefits including but not limited to financial support, pet insurance, mental health resources, volunteer paid days off, employee stock program, foundation donation matching, and much more
- Opportunity for annual cash bonus (maybe under Pay? but could be benefit). We'll treat annual cash bonus as part of Pay.
Pay: Salary range starting at $70,000 based on experience; Opportunity for annual cash bonus.
Schedule: hybrid role based out of Westbrook, Maine headquarters, with an expectation of 2 days onsite per week.
We should not repeat the hybrid description in About? Already included there but it's okay to repeat? Might be duplicate. Better to keep schedule separate and maybe not duplicate in About. But About already includes hybrid description; we could keep it there and not repeat in Schedule. However schedule section expects specific content; we have it. Could keep schedule as "Hybrid role: 2 days onsite per week at Westbrook, Maine headquarters." That's not duplicate of About? About already says hybrid role based out of Westbrook, Maine headquarters, with expectation of 2 days onsite per week. If we include both, it's duplicate. To avoid duplication, we could move the hybrid detail to Schedule and remove from About. But About currently includes that detail; we could keep About more generic about role purpose, team, why matters, and move location/schedule to Schedule. Let's adjust About to exclude location/schedule.
Rewrite About: focus on role purpose, team, why matters, opportunity.
Let's craft About paragraph:
"As a Data Engineer, you will help build and scale the data infrastructure that powers analytics, reporting, AI-enabled products, and business decision-making across IDEXX. The Global Sales and Service Analytics team develops and supports the data products, governance frameworks, analytical solutions, and AI capabilities that empower stakeholders ranging from Sales Professionals to Executive Leadership around the world. Why does this role matter? This role sits at the intersection of modern data engineering and artificial intelligence, giving you the opportunity to directly influence how data is transformed into insights, products, and business outcomes. You'll join a highly collaborative team working with a modern cloud and AI technology stack to develop scalable data pipelines, support advanced analytics, and build AI-enabled solutions. Your work will have visibility across the organization and play a key role in advancing data-driven decision making, innovation, and operational excellence. This is an excellent opportunity for an early-career data engineer to expand technical expertise, take on increasing ownership, and help shape the future of data and AI at IDEXX."
That excludes location/schedule.
Now Schedule: "Hybrid role based out of Westbrook, Maine headquarters with an expectation of 2 days onsite per week."
Now Benefits: list as above.
Pay: "Salary range starting at $70,000 based on experience; Opportunity for annual cash bonus."
Now we need to ensure we don't include boilerplate like EEO etc. We'll cut everything after "Let’s pursue what matters together." and the EEO paragraph.
Now produce HTML.
Structure:
About the role
...
Responsibilities
- ...
...
Requirements
- ...
...
Qualifications
- ...
...
Benefits
- ...
...
Pay
...
Schedule
...
Make sure no extra whitespace issues.
Let's craft the lists precisely.
Responsibilities list items as above.
Requirements list items as above.
Qualifications list items: combine strongly preferred and preferred.
Let's list:
- 1 to 3 years of relevant data engineering experience and are eager to expand your technical expertise and ownership.
- Experience with dbt for data transformation and modeling.
- Experience with workflow orchestration tools such as Dagster.
- Experience working within AWS and Snowflake environments.
- Experience building and supporting CI/CD pipelines using GitHub and GitHub Actions.
- Familiarity with software APIs and data integration patterns.
- Experience working in Agile or Scrum development environments.
- Exposure to AI, machine learning, or large language model technologies and are excited about applying them to data products and workflows.
- Experience with Databricks, Snowflake Cortex, AI agents, or similar AI-enabled platforms.
- Demonstrate intellectual curiosity, a bias toward automation, and enthusiasm for modern data engineering technologies.
- Experience supporting
for prose. Do not repeat job title as heading. Do not include Job ID etc. Remove boilerplate: EEO, equal opportunity, scam warning, navigation, cookie, legal, duplicated lines, application instructions. Also we should not create sections unless posting has real specific content. We have content for About the role (first paragraph). Responsibilities (the "In This Role You will ..." bullet list). Requirements (the "What You Need To Succeed" list). Qualifications? Might be overlapping with Requirements; but we can treat Requirements as required qualifications. Qualifications maybe for preferred? The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Skills). We need to decide which sections to include based on content. We have: - About the role: first paragraph describing role, hybrid, team, why matters. - Responsibilities: bullet list of duties. - Requirements: list of required items (the "What You Need To Succeed" required items). Also there are "Strongly Preferred" and "Preferred". Those could go under Qualifications or Skills. The instruction: Use sections only when posting has real specific content. We have content for required, preferred, skills. Could combine into Requirements (required) and Qualifications (preferred) and Skills (maybe technical skills). But we have many items overlapping. Better to follow typical structure: About the role, Responsibilities, Requirements (required qualifications), Qualifications (preferred), Skills (maybe technical skills), Benefits, Pay, Schedule. We have Pay: "Salary range starting at $70,000 based on experience". Also mention annual cash bonus. That's pay info. Schedule: hybrid role based out of Westbrook, Maine headquarters, expectation of 2 days onsite per week. That's schedule. Benefits: list of benefits: Health/Dental/Vision, Day-One 5% matching 401k, additional benefits including financial support, pet insurance, mental health resources, volunteer paid days off, employee stock program, foundation donation matching, etc. We need to avoid filler like "commensurate with experience" etc. We have actual numbers. We must not include duplicated lines. Also remove EEO etc. We must output only HTML fragment, no /, no markdown fences. Let's extract content. About the role: combine first few paragraphs up to "Why does this role matter?" maybe include that as part of About. Let's capture: "As a Data Engineer, you will help build and scale the data infrastructure that powers analytics, reporting, AI-enabled products, and business decision-making across IDEXX. This is a hybrid role based out of our Westbrook, Maine headquarters, with an expectation of 2 days onsite per week. The Global Sales and Service Analytics team develops and supports the data products, governance frameworks, analytical solutions, and AI capabilities that empower stakeholders ranging from Sales Professionals to Executive Leadership around the world. Why does this role matter? This role sits at the intersection of modern data engineering and artificial intelligence, giving you the opportunity to directly influence how data is transformed into insights, products, and business outcomes. You'll join a highly collaborative team working with a modern cloud and AI technology stack to develop scalable data pipelines, support advanced analytics, and build AI-enabled solutions. Your work will have visibility across the organization and play a key role in advancing data-driven decision making, innovation, and operational excellence. This is an excellent opportunity for an early-career data engineer to expand technical expertise, take on increasing ownership, and help shape the future of data and AI at IDEXX." That seems good. Responsibilities: list each bullet starting with "You will". We'll extract each sentence. List: - design and build scalable, reliable data pipelines that support analytics, AI applications, dashboards, and enterprise reporting solutions. - develop and maintain SQL and Python code that enables efficient data transformation, integration, testing, and validation. - contribute to the development and governance of enterprise data models that promote consistency, scalability, and reuse across multiple products and solutions. - help manage automated deployment processes and support continuous integration and delivery practices for data engineering solutions. - partner with analysts, developers, and business stakeholders to understand data requirements and deliver trusted data assets. - support data architecture initiatives by helping define standards for how data is stored, integrated, accessed, and managed within cloud environments. - apply AI and large language model technologies within data pipelines and analytical products to improve efficiency and unlock new capabilities. - build and enhance AI-powered agents and solutions that support business users and analytical workflows. - participate in code reviews and contribute to data governance practices that promote quality, maintainability, and consistency. - monitor and optimize pipeline performance, troubleshoot issues, and implement improvements aligned with team standards. - resolve production issues, enhancements, and development requests while balancing evolving business priorities. - collaborate closely with both technical and non-technical stakeholders to deliver impactful data solutions. We'll put each as
About the role
...
Responsibilities
- ... ...
Requirements
- ... ...
Qualifications
- ... ...
Benefits
- ... ...
Pay
...
Schedule
...
Make sure no extra whitespace issues. Let's craft the lists precisely. Responsibilities list items as above. Requirements list items as above. Qualifications list items: combine strongly preferred and preferred. Let's list: - 1 to 3 years of relevant data engineering experience and are eager to expand your technical expertise and ownership. - Experience with dbt for data transformation and modeling. - Experience with workflow orchestration tools such as Dagster. - Experience working within AWS and Snowflake environments. - Experience building and supporting CI/CD pipelines using GitHub and GitHub Actions. - Familiarity with software APIs and data integration patterns. - Experience working in Agile or Scrum development environments. - Exposure to AI, machine learning, or large language model technologies and are excited about applying them to data products and workflows. - Experience with Databricks, Snowflake Cortex, AI agents, or similar AI-enabled platforms. - Demonstrate intellectual curiosity, a bias toward automation, and enthusiasm for modern data engineering technologies. - Experience supporting